aditya ramesh openai
http://adityaramesh.com/about.html
about
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<div><article>
<section>
<h2>About Me</h2>
<p>I created <a href="https://openai.com/blog/dall-e">DALL·E</a> and co-created <a href="https://openai.com/dall-e-2">DALL·E 2</a>, a system for text-to-image generation developed at <a href="https://openai.com">OpenAI.</a> I’m interested in using AI to amplify human creativity.</p>
<p><a href="https://twitter.com/model_mechanic">Find me on Twitter here</a> or reach me by email at <code>_@adityaramesh.com</code>.</p>
</section>
</article></div>||||I||||About Posts

About Me

I created DALL·E and co-created DALL·E 2, a system for text-to-image generation developed at OpenAI. I’m interested in using AI to amplify human creativity.

Find me on Twitter here or reach me by email at _@adityaramesh.com.

 Tufte-Blog uses Pandoc with Tufte CSS and MathJax.
About Me
I created DALL·E and co-created DALL·E 2, a system for text-to-image generation developed at OpenAI. I’m interested in using AI to amplify human creativity.
 Find me on Twitter here or reach me by email at _@adityaramesh.com.
about
http://adityaramesh.com/about.html
https://www.linkedin.com/in/aditya-ramesh-582a3947/
Aditya Ramesh - LightBeam.ai | LinkedIn
2024-01-01
Aditya Ramesh
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 * It's been amazing to see how our unique approach to data protection and governance has helped customers across multiple sectors. This investment in…

 It's been amazing to see how our unique approach to data protection and governance has helped customers across multiple sectors. This investment in…

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 * We are proud to announce that our seed portfolio company Lightbeam.ai has come out of stealth. This is, surprisingly, our first major bet from 8VC…

 We are proud to announce that our seed portfolio company Lightbeam.ai has come out of stealth. This is, surprisingly, our first major bet from 8VC…

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 * Thanks for believing in us! It is a real asset to have the backing of visionaries like Bhaskar Ghosh who have both the business acumen and are also…

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About
 
 
 Software engineer specializing in building large-scale distributed systems.
 
 
 
 
 
 
 Activity
 
 
 Experience &amp; Education
 
 
 
 LightBeam.ai
 
 
View Aditya’s full experience
See their title, tenure and more.
 Languages
 
 More activity by Aditya
 
 Other similar profiles
 
 
 Himanshu Shukla
 
 
 San Jose, CA
 
 Connect
 
 
 Priyadarshi Prasad
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Prafulla Dhariwal
 
 
 San Francisco, CA
 
 Connect
 
 
 Tim Brooks
 
 
 San Francisco, CA
 
 Connect
 
 
 Bill Peebles
 
 
 
 Sora Research Lead at OpenAI
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Sandhini Agarwal
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Abhinay Nagpal
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Ramesh Aditya
 
 
 
 Senior General Manager - Senior Procurement Category Manager - Facilities and Real Estate Category
 
 
 Bengaluru
 
 Connect
 
 
 Arvind Neelakantan
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Rohan Sahai
 
 
 San Francisco, CA
 
 Connect
 
 Connect
 
 
 Ramesh A Aditya
 
 
 
 Head Of Strategy at Shankar IAS Academy
 
 
 Tamil Nadu, India
 
 Connect
 
 
 Karan Talreja
 
 
 
 Senior Software Engineer at Lightbeam.ai
 
 
 San Jose, CA
 
 Connect
 
 
 Gabriel Goh
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Reid Hoffman
 
 
 United States
 
 Connect
 
 Connect
 
 
 Reynold Xin
 
 
 San Francisco, CA
 
 Connect
 
 
 Michael Starman
 
 
 Los Gatos, CA
 
 Connect
 
 
 Frank Kane
 
 
 Merritt Island, FL
 
 Connect
 
 
 Soham Mehta
 
 
 Sunnyvale, CA
 
 Connect
 
 Explore collaborative articles
 
 We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
 
 Explore More
 
 Others named Aditya Ramesh in United States
 11 others named Aditya Ramesh in United States are on LinkedIn
 
 
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Aditya Ramesh - LightBeam.ai | LinkedIn
https://www.linkedin.com/in/aditya-ramesh-582a3947/
https://www.linkedin.com/in/adityaramesh17/
Aditya Ramesh - Cisco | LinkedIn
2020-03-05
Aditya Ramesh
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 About
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 I am a highly motivated vision engineer/ roboticist passionate about building autonomous…
 
 
 
 
 
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 Activity
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 Experience &amp; Education
 
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 Cisco
 
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 Publications
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<a href="https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fopenaccess%2Ethecvf%2Ecom%2Fcontent_WACV_2020%2Fhtml%2FRaman_CompressNet_Generative_Compression_at_Extremely_Low_Bitrates_WACV_2020_paper%2Ehtml&amp;urlhash=f_pM&amp;trk=public_profile_publication-title">
 CompressNet : Generative Image Compression at Extremely Low Bitrates
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 Compressing images at extremely low bitrates (&lt; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (&lt; 0.1 bpp). In…
 
 
 
 
 
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 Compressing images at extremely low bitrates (&lt; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (&lt; 0.1 bpp). In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods.
 
 
 
 
 
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<p><a href="https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fopenaccess%2Ethecvf%2Ecom%2Fcontent_WACV_2020%2Fhtml%2FRaman_CompressNet_Generative_Compression_at_Extremely_Low_Bitrates_WACV_2020_paper%2Ehtml&amp;urlhash=f_pM&amp;trk=public_profile_publication-button">
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<a href="https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fwww%2Enikolabanovic%2Enet%2Fbeing_trustworthy_is_not_enough%2Epdf&amp;urlhash=P7ne&amp;trk=public_profile_publication-title">
 Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust
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 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,<br/>and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to<br/>exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy<br/>AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two…
 
 
 
 
 
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 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,<br/>and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to<br/>exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy<br/>AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent,<br/>fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in three<br/>games against another engine opponent. We varied coaches’ transparency about their competence (with the<br/>untrustworthy one always exaggerating its competence). We quantified and objectively measured participants’<br/>trust based on how often participants relied on coaches’ move recommendations. Participants showed inability<br/>to assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive.<br/>Our work calls for design of interactions to help end-users assess AI trustworthiness.
 
 
 
 
 
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<p><a href="https://www.linkedin.com/redir/redirect?url=http%3A%2F%2Fwww%2Enikolabanovic%2Enet%2Fbeing_trustworthy_is_not_enough%2Epdf&amp;urlhash=P7ne&amp;trk=public_profile_publication-button">
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<a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fieeexplore%2Eieee%2Eorg%2Fdocument%2F8995532&amp;urlhash=AiQM&amp;trk=public_profile_publication-title">
 Clustering of Driving Scenarios Using Connected Vehicle Datasets 
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 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal…
 
 
 
 
 
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 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles.
 
 
 
 
 
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<p><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fieeexplore%2Eieee%2Eorg%2Fdocument%2F8995532&amp;urlhash=AiQM&amp;trk=public_profile_publication-button">
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 Patents
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 Artificial neural networks 
 
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 Computational HCI
 
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 Computational motion planning
 
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 Convolutional neural networks for visual recognition
 
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 CS231n
 
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 Deep Learning
 
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 EECS598
 
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 Foundations of Computer Vision
 
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 EECS504
 
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 Introduction to machine learning - Course era 
 
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 Math for robotics
 
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 ROB 501
 
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 Mobile robotics
 
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 Robotics laboratory
 
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 ROB 550
 
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 Self driving cars - control and perception
 
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 Projects
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 Mobile robot
 
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 Built a mobile robot and implemented localisation using SLAM. A LIDAR and occupancy grid mapping algorithm was used. Variants of A* search for path planning was used.
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 Vehicle detection
 
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 Implemented a deep neural network pipeline for identification of the number of cars in a video stream. Used a combination of DeepLab segmentation network and Convolutional Neural Network.
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 Autonomous self balancing robot
 
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 Used PID based control algorithms, path planning using potential fields and localization using Motion capture systems from optitrack to control the robot autonomously.
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 Autonomous robotic arm
 
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 Developed a constraint based kinematics algorithm for a 6 DoF robotic arm. Perception was achieved using algorithms on OpenCV and a Kinect sensor.
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 Cluster analysis 
 
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 Developing a clustering algorithm for<br/> automatization of cluster number detection and robust clustering.
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 Implementation of high accuracy on-chip diagnosis design
 
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 Using machine learning algorithms for on chip module level diagnosis.
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 Courier service robot
 
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 Using djkstras algorithm, to come up with optimized delivery and pick up of packages in a real life scenario. Used raspberry pi Bot and camera for path detection and package pick up and delivery.
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 Reinforcement learning based control of non linear systems.
 
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 Using reinforcement learning methods and artificial neural networks for control of non linear systems expressed in non strict feedback form.
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 Face recognition using artifical neural networks.
 
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 Using HOG filters for feature extraction and a two layered ANN to detect faces. Generated and used our own datasets.
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 BLE based indoor tracking system
 
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 Using Bluetooth low energy devices for indoor tracking for health care personnel in a hospital.
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 Honors &amp; Awards
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 SUMMER RESEARCH FELLOW 
 
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 Indian academy of sciences 
 
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 Was awarded the summer research fellowship under the Indian academy of sciences summer research fellowship programme.
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 Winners of Arduino programming challenge 
 
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 IIT Madras
 
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 Using the arduino board and building a small working model of a morse code transciever.
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 Winners of Microthon robotics challenge
 
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 IIT Madras
 
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 Building a PID based intelligent line follower that chooses the optimal (shortest) path to reach destination.
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 More activity by Aditya
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<p>
 State College, PA
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 United States
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 Aditya Ramesh

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 Sunnyvale, California, United States Contact Info

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 Cisco
 University of Michigan - Rackham Graduate School
 * Report this profile

 About

 I am a highly motivated vision engineer/ roboticist passionate about building autonomous…
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 * We're releasing two free Colab notebooks for 2x faster & 50% less memory finetuning for Phi-3 & ORPO! Phi-3 is Microsoft's latest mini model…

 We're releasing two free Colab notebooks for 2x faster & 50% less memory finetuning for Phi-3 & ORPO! Phi-3 is Microsoft's latest mini model…

 Liked by Aditya Ramesh

 * MyVLM code is out! We put online the code, the first dataset for personalized large visual-language models, and our pretrained models. Check it…

 MyVLM code is out! We put online the code, the first dataset for personalized large visual-language models, and our pretrained models. Check it…

 Liked by Aditya Ramesh

 * I will be attending #AISTATS2024 this week to present our paper. In this work, we propose the first theoretically rigorous data selection method for…

 I will be attending #AISTATS2024 this week to present our paper. In this work, we propose the first theoretically rigorous data selection method for…

 Liked by Aditya Ramesh

 Join now to see all activity

 Experience & Education

 * Cisco

 ****** ** ********

 * ****

 ******* ********** - ******** ******

 * ******

 ******** ****** *********

 * ********** ** ******** - ******* ******** ******

 **** ******* ********** *** ******** ******* *********** (****** / ********)

 * *** **********

 ******** ** *********** (**) ****************** *********** *.**

 2013 - 2017

 View Aditya’s full experience

 See their title, tenure and more.

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 Publications

 * CompressNet : Generative Image Compression at Extremely Low Bitrates

 IEEE/CVF Winter Conference on Applications of Computer Vision March 5, 2020

 Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (< 0.1 bpp). In… Show more

 Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (< 0.1 bpp). In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods. Show less

 See publication

 * Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust

 Proceedings of the ACM on Human-Computer Interaction

 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,
 and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to
 exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy
 AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two… Show more

 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,
 and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to
 exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy
 AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent,
 fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in three
 games against another engine opponent. We varied coaches’ transparency about their competence (with the
 untrustworthy one always exaggerating its competence). We quantified and objectively measured participants’
 trust based on how often participants relied on coaches’ move recommendations. Participants showed inability
 to assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive.
 Our work calls for design of interactions to help end-users assess AI trustworthiness. Show less

 See publication

 * Clustering of Driving Scenarios Using Connected Vehicle Datasets

 IEEE transactions on Intelligent Vehicles

 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal… Show more

 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles. Show less

 See publication

 Patents

 * Unsupervised Clustering of Encountering Scenarios Using Connected Vehicle Dataset

 Issued December 7, 2021 US 11194331

 See patent

 Courses

 * Artificial neural networks

 -

 * Computational HCI

 -

 * Computational motion planning

 -

 * Convolutional neural networks for visual recognition

 CS231n

 * Deep Learning

 EECS598

 * Foundations of Computer Vision

 EECS504

 * Introduction to machine learning - Course era

 -

 * Math for robotics

 ROB 501

 * Mobile robotics

 EECS565

 * Robotics laboratory

 ROB 550

 * Self driving cars - control and perception

 ROB 598

 Projects

 * Mobile robot

 Nov 2017 - Dec 2017

 Built a mobile robot and implemented localisation using SLAM. A LIDAR and occupancy grid mapping algorithm was used. Variants of A* search for path planning was used.

 * Vehicle detection

 Nov 2017 - Dec 2017

 Implemented a deep neural network pipeline for identification of the number of cars in a video stream. Used a combination of DeepLab segmentation network and Convolutional Neural Network.

 * Autonomous self balancing robot

 Sep 2017 - Oct 2017

 Used PID based control algorithms, path planning using potential fields and localization using Motion capture systems from optitrack to control the robot autonomously.

 * Autonomous robotic arm

 Aug 2017 - Oct 2017

 Developed a constraint based kinematics algorithm for a 6 DoF robotic arm. Perception was achieved using algorithms on OpenCV and a Kinect sensor.

 * Cluster analysis

 Dec 2016 - Sep 2017

 Developing a clustering algorithm for
 automatization of cluster number detection and robust clustering.

 * Implementation of high accuracy on-chip diagnosis design

 Jun 2016 - Dec 2016

 Using machine learning algorithms for on chip module level diagnosis.

 * Courier service robot

 Jan 2016 - May 2016

 Using djkstras algorithm, to come up with optimized delivery and pick up of packages in a real life scenario. Used raspberry pi Bot and camera for path detection and package pick up and delivery.

 Other creators
 + 

 * Reinforcement learning based control of non linear systems.

 Jan 2016 - May 2016

 Using reinforcement learning methods and artificial neural networks for control of non linear systems expressed in non strict feedback form.

 * Face recognition using artifical neural networks.

 Aug 2015 - Dec 2015

 Using HOG filters for feature extraction and a two layered ANN to detect faces. Generated and used our own datasets.

 Other creators
 + 
 + 

 * BLE based indoor tracking system

 Jun 2015 - Aug 2015

 Using Bluetooth low energy devices for indoor tracking for health care personnel in a hospital.

 Honors & Awards

 * SUMMER RESEARCH FELLOW

 Indian academy of sciences

 May 2015

 Was awarded the summer research fellowship under the Indian academy of sciences summer research fellowship programme.

 * Winners of Arduino programming challenge

 IIT Madras

 Using the arduino board and building a small working model of a morse code transciever.

 * Winners of Microthon robotics challenge

 IIT Madras

 Building a PID based intelligent line follower that chooses the optimal (shortest) path to reach destination.

 More activity by Aditya

 * We are thrilled to announce the release of OpenBioLLM-Llama3-70B & 8B from Saama AI Research Labs. These groundbreaking models outperform Openai’s…

 We are thrilled to announce the release of OpenBioLLM-Llama3-70B & 8B from Saama AI Research Labs. These groundbreaking models outperform Openai’s…

 Liked by Aditya Ramesh

 * Rappel aux "experts IA" qui poussent comme des 🍄 sur Linkedin: Croire que vous faites de l'IA parce que vous faites des prompts ChatGPT, c'est…

 Rappel aux "experts IA" qui poussent comme des 🍄 sur Linkedin: Croire que vous faites de l'IA parce que vous faites des prompts ChatGPT, c'est…

 Liked by Aditya Ramesh

 * Hi all, We have multiple openings at Engineer/Senior/Staff engineer level in Physical Design group in Arm involved in Physical implementation of…

 Hi all, We have multiple openings at Engineer/Senior/Staff engineer level in Physical Design group in Arm involved in Physical implementation of…

 Liked by Aditya Ramesh

 * Nikhil Kamath is destroying the B-school guest lecturer industry. . . . His WTF Podcast is a goldmine of actionable insights aimed at young minds…

 Nikhil Kamath is destroying the B-school guest lecturer industry. . . . His WTF Podcast is a goldmine of actionable insights aimed at young minds…

 Liked by Aditya Ramesh

 * Launching Lean Co-pilot for LLM-human collaboration to write formal mathematical proofs that are 100% accurate. We use LLMs to suggest proof tactics…

 Launching Lean Co-pilot for LLM-human collaboration to write formal mathematical proofs that are 100% accurate. We use LLMs to suggest proof tactics…

 Liked by Aditya Ramesh

 * It’s merely been a week since Meta launched their Llama3 model, and MSFT has introduced Phi3, their smallest model yet. It surpasses Llama3 on…

 It’s merely been a week since Meta launched their Llama3 model, and MSFT has introduced Phi3, their smallest model yet. It surpasses Llama3 on…

 Liked by Aditya Ramesh

 * Deeply concerned with the skillset quality of recent graduates (across all tiers of colleges). After screening hundreds of profiles, assessments and…

 Deeply concerned with the skillset quality of recent graduates (across all tiers of colleges). After screening hundreds of profiles, assessments and…

 Liked by Aditya Ramesh

 * Introducing “🍷Fineweb” Llama3 was trained on 15 trillion tokens of public data. But where can you find such large datasets and recipes? Here comes…

 Introducing “🍷Fineweb” Llama3 was trained on 15 trillion tokens of public data. But where can you find such large datasets and recipes? Here comes…

 Liked by Aditya Ramesh

 * Two years ago, I faced an unexpected hurdle. After months of diligent preparation, I was barred from taking the GMAT because my registered name did…

 Two years ago, I faced an unexpected hurdle. After months of diligent preparation, I was barred from taking the GMAT because my registered name did…

 Liked by Aditya Ramesh

 * AI research is currently dominated by a few extremely well-funded companies. Australian AI researcher Jeremy Howard wants to change that. In…

 AI research is currently dominated by a few extremely well-funded companies. Australian AI researcher Jeremy Howard wants to change that. In…

 Liked by Aditya Ramesh

 * 🥁 Llama3 is out 🥁 8B and 70B models available today. 8k context length. Trained with 15 trillion tokens on a custom-built 24k GPU cluster. Great…

 🥁 Llama3 is out 🥁 8B and 70B models available today. 8k context length. Trained with 15 trillion tokens on a custom-built 24k GPU cluster. Great…

 Liked by Aditya Ramesh

 * Really excited to officially release torchtune - a PyTorch-native library for easily fine-tuning LLMs! Code:…

 Really excited to officially release torchtune - a PyTorch-native library for easily fine-tuning LLMs! Code:…

 Liked by Aditya Ramesh

 * Our AI is Accredited. GumGum is the first ad tech provider to get accreditation from MRC for Content-Level Analysis for Connected TV, Desktop and…

 Our AI is Accredited. GumGum is the first ad tech provider to get accreditation from MRC for Content-Level Analysis for Connected TV, Desktop and…

 Liked by Aditya Ramesh

 * For folks who are graduating soon, there are some exciting AI/ML opportunities in Outshift I'd strongly recommend applying for. This is one of the…

 For folks who are graduating soon, there are some exciting AI/ML opportunities in Outshift I'd strongly recommend applying for. This is one of the…

 Liked by Aditya Ramesh

 * One day PhDs will animate every object around us with reinforcement learning to keep their thesis going 🤣

 One day PhDs will animate every object around us with reinforcement learning to keep their thesis going 🤣

 Liked by Aditya Ramesh

 * At the end of the Y Combinator batch party, on our way to deploy V2 of the Artisan platform, we got stuck in an elevator at the Transamerica Pyramid…

 At the end of the Y Combinator batch party, on our way to deploy V2 of the Artisan platform, we got stuck in an elevator at the Transamerica Pyramid…

 Liked by Aditya Ramesh

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About
 
 
 I am a highly motivated vision engineer/ roboticist passionate about building autonomous…
 
 
 
 
 
 
 Activity
 
 
 Experience &amp; Education
 
 
 
 Cisco
 
 
View Aditya’s full experience
See their title, tenure and more.
 Publications
 
 CompressNet : Generative Image Compression at Extremely Low Bitrates
 
 Compressing images at extremely low bitrates (&lt; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (&lt; 0.1 bpp). In…
 
 
 
 
 
 
 Compressing images at extremely low bitrates (&lt; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bitrates (&lt; 0.1 bpp). In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods.
 
 
 
 
 
 
 
 See publication
 
 
 
 Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust
 
 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI toexaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthyAI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two…
 
 
 
 
 
 
 Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency,and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI toexaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthyAI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive endusers to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent,fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in threegames against another engine opponent. We varied coaches’ transparency about their competence (with theuntrustworthy one always exaggerating its competence). We quantified and objectively measured participants’trust based on how often participants relied on coaches’ move recommendations. Participants showed inabilityto assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive.Our work calls for design of interactions to help end-users assess AI trustworthiness.
 
 
 
 
 
 
 
 See publication
 
 
 
 Clustering of Driving Scenarios Using Connected Vehicle Datasets 
 
 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal…
 
 
 
 
 
 
 This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles.
 
 
 
 
 
 
 
 See publication
 
 
 
 Patents
 
 Courses
 
 
 Artificial neural networks 
 
 
-
 
 Computational HCI
 
 
-
 
 Computational motion planning
 
 
-
 
 Convolutional neural networks for visual recognition
 
 
 
 CS231n
 
 
 
 Deep Learning
 
 
 
 EECS598
 
 
 
 Foundations of Computer Vision
 
 
 
 EECS504
 
 
 
 Introduction to machine learning - Course era 
 
 
-
 
 Math for robotics
 
 
 
 ROB 501
 
 
 
 Mobile robotics
 
 
 
 EECS565
 
 
 
 Robotics laboratory
 
 
 
 ROB 550
 
 
 
 Self driving cars - control and perception
 
 
 
 ROB 598
 
 
 Projects
 
 
 Mobile robot
 
 
 Built a mobile robot and implemented localisation using SLAM. A LIDAR and occupancy grid mapping algorithm was used. Variants of A* search for path planning was used.
 
 
 Vehicle detection
 
 
 Implemented a deep neural network pipeline for identification of the number of cars in a video stream. Used a combination of DeepLab segmentation network and Convolutional Neural Network.
 
 
 Autonomous self balancing robot
 
 
 Used PID based control algorithms, path planning using potential fields and localization using Motion capture systems from optitrack to control the robot autonomously.
 
 
 Autonomous robotic arm
 
 
 Developed a constraint based kinematics algorithm for a 6 DoF robotic arm. Perception was achieved using algorithms on OpenCV and a Kinect sensor.
 
 
 Cluster analysis 
 
 
 Developing a clustering algorithm for automatization of cluster number detection and robust clustering.
 
 
 Implementation of high accuracy on-chip diagnosis design
 
 
 Using machine learning algorithms for on chip module level diagnosis.
 
 
 Courier service robot
 
 
 Using djkstras algorithm, to come up with optimized delivery and pick up of packages in a real life scenario. Used raspberry pi Bot and camera for path detection and package pick up and delivery.
 
Other creators
 
 Reinforcement learning based control of non linear systems.
 
 
 Using reinforcement learning methods and artificial neural networks for control of non linear systems expressed in non strict feedback form.
 
 
 Face recognition using artifical neural networks.
 
 
 Using HOG filters for feature extraction and a two layered ANN to detect faces. Generated and used our own datasets.
 
Other creators
 
 BLE based indoor tracking system
 
 
 Using Bluetooth low energy devices for indoor tracking for health care personnel in a hospital.
 
 Honors &amp; Awards
 
 
 SUMMER RESEARCH FELLOW 
 
 
 
 Indian academy of sciences 
 
 
  
 Was awarded the summer research fellowship under the Indian academy of sciences summer research fellowship programme.
 
 
 Winners of Arduino programming challenge 
 
 
 
 IIT Madras
 
 
 Using the arduino board and building a small working model of a morse code transciever.
 
 
 Winners of Microthon robotics challenge
 
 
 
 IIT Madras
 
 
 Building a PID based intelligent line follower that chooses the optimal (shortest) path to reach destination.
 
 More activity by Aditya
 
 Other similar profiles
 
 
 Akshay Kalkunte Suresh
 
 
 San Francisco Bay Area
 
 Connect
 
 Connect
 
 
 Prateek Joshi
 
 
 San Francisco Bay Area
 
 Connect
 
 
 C R Madhavan
 
 
 Sunnyvale, CA
 
 Connect
 
 
 Bill Peebles
 
 
 
 Sora Research Lead at OpenAI
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Anirudh Sreeram
 
 
 
 Machine Learning engineering Intern (NLP) @ Servicenow | Graduate Research Assistant @ SAIL lab, USC | Ex Research Assistant @ LEAP lab, IISc | Ex Project Engineer @ WIPRO
 
 
 Los Angeles, CA
 
 Connect
 
 
 Tim Brooks
 
 
 San Francisco, CA
 
 Connect
 
 
 Prafulla Dhariwal
 
 
 San Francisco, CA
 
 Connect
 
 
 Gabriel Goh
 
 
 San Francisco Bay Area
 
 Connect
 
 
 Neeraj Sajjan
 
 
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2024-01-01
Aishwarya Ramesh
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 Denver, CO
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 * Why are groceries so expensive? I spoke with Food & Wine editor Micheline Maynard and shed some light on the lingering effects of higher food prices…

 Why are groceries so expensive? I spoke with Food & Wine editor Micheline Maynard and shed some light on the lingering effects of higher food prices…

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 * Our CEO Joel Rampoldt sat down with Bridget Goldschmidt to share his plans for Lidl US and how he plans to realize our full potential. #teamlidl…

 Our CEO Joel Rampoldt sat down with Bridget Goldschmidt to share his plans for Lidl US and how he plans to realize our full potential. #teamlidl…

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 * This #internationalwomensday, I'm grateful to be surrounded by so many strong women in the produce industry who inspire and encourage me daily. I'm…

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 * Just finished the course “Become a Courageous Female Leader”! Check it out: https://lnkd.in/gx8Dnjhp

 Just finished the course “Become a Courageous Female Leader”! Check it out: https://lnkd.in/gx8Dnjhp

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 * Couldn’t be prouder of Rosalind Chow for giving a Presidential Plenary address at the annual meeting of the Society for Personality and Social…

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 * Excited to share that I'll be representing AlixPartners at LINK 2024: The Retail Supply Chain Conference in February. Fun fact: Our recently released…

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 * I'm excited to announce that I’ve been selected as a Partner and Managing Director at AlixPartners. A huge thanks to everyone I've worked with for…

 I'm excited to announce that I’ve been selected as a Partner and Managing Director at AlixPartners. A huge thanks to everyone I've worked with for…

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 * AlixPartners’ annual Cost Inflation Outlook presents a fresh set of market perspectives, predictions and expert insights for Consumer Products (CP)…

 AlixPartners’ annual Cost Inflation Outlook presents a fresh set of market perspectives, predictions and expert insights for Consumer Products (CP)…

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 Greater Chicago Area
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 Norfolk, VA

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 * Aishwarya Ramesh

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Activity
 
 
 Experience &amp; Education
 
 
 
 AlixPartners
 
 
View Aishwarya’s full experience
See their title, tenure and more.
 More activity by Aishwarya
 
 People also viewed
 
 
 Aditi Mahajan
 
 
 Greater Philadelphia
 
 Connect
 
 
 Shilpa M.
 
 
 
 Management Consultant at AlixPartners
 
 
 Chicago, IL
 
 Connect
 
 
 Florencia Jacobacci
 
 
 
 Data science and analytics | Data engineering | Digital transformation | Biomedical Engineer | Ph.D. in neuroscience
 
 
 Argentina
 
 Connect
 
 
 Emilia Kanazireva
 
 
 Greater Chicago Area
 
 Connect
 
 
 Sunita Das
 
 
 Detroit Metropolitan Area
 
 Connect
 
 Connect
 
 
 Aditya Eswar
 
 
 
 SVP at AlixPartners | Private Equity | Performance Improvement
 
 
 Chicago, IL
 
 Connect
 
 
 David Yaffe
 
 
 
 Management Consultant | SVP at AlixPartners | Carnegie Mellon Tepper MBA
 
 
 Denver, CO
 
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Aditya Ramesh – Medium
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Aditya Ramesh
<div><div><p>Former Software Engineering Intern, Cisco, Deloitte | Published Algorithm Blogger, Topcoder | <a href="http://rameshaditya.com">rameshaditya.com</a> | <a href="http://blogarithms.github.io">blogarithms.github.io</a> | <a href="http://github.com/RameshAditya">github.com/RameshAditya</a></p></div></div>||||I|||| Open in app

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 Aditya Ramesh

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 Aditya Ramesh

 Data Analytics On Pokemon!

 Using Python to reveal insights into the world of Pokemon!

 6 min read · Nov 30, 2018

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 DataDrivenInvestor

 Why Every Developer Should Blog

 This post was originally published on my personal blog — https://blogarithms.github.io/ — do check it out for technical blog posts!

 4 min read · Nov 16, 2018

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 Aditya Ramesh

 An introduction to Segment Trees

 Originally posted at: rameshaditya.github.io/algorithmic-memoirs/traversing-segment-trees.html

 4 min read · Nov 30, 2017

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 Aditya Ramesh

 11 Followers

 Former Software Engineering Intern, Cisco, Deloitte | Published Algorithm Blogger, Topcoder | rameshaditya.com | blogarithms.github.io | github.com/RameshAditya

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RameshAditya - Overview
2022-02-06
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 <p>Building, learning and writing stuff.</p>
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 </p><h2>
 
 Aditya Ramesh
 
 
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 Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)
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 RameshAditya Follow
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 Aditya Ramesh RameshAditya

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 Overview Repositories 28 Projects 0 Packages 0 Stars 67
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 Pinned

 1. asciify Public

 Convert any image into ASCII Art.

 Python 1k 157

 2. scoper Public

 Fuzzy and semantic search for captioned YouTube videos.

 Python 249 15

 3. code-faster Public

 Sublime Text plugin for programming contests - loads codeforces problems right into your text editor with a single button.

 Python 38 4

 4. ducky Public

 Chrome extension to overlay a (super adorable) rubber duck, as a virtual companion during rubber duck debugging.

 JavaScript 80 9

 5. cp-algorithms / cp-algorithms Public

 Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)

 C++ 4.2k 988

 6. markov-sherlock-holmes-twitterbot Public

 Markov model manually implemented that predicts words probabilistically based on historic data.

 Python 6 2

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Building, learning and writing stuff.
 
 
 
  
 
 Aditya Ramesh
 
 
 RameshAditya
 
 
  
 
 
 Building, learning and writing stuff.
 
 
  Software Engg • Ex-Algo Blogger, Topcoder • Personal Algo blog - https://blogarithms.github.io/  
 
 
 
 India
 
 https://rameshaditya.com
 
 
 
 
 
 
 
 Block or Report
 
 
 
 Block or report RameshAditya
 
 
 
  
 
 
 
 
 
 Pinned
 
 
 
 
 
 
 
 
 
 
 Convert any image into ASCII Art. 
 
 
 
 
 Python
 
 
 
 1k
 
 
 
 
 157
 
 
 
 
 
 
 
 Fuzzy and semantic search for captioned YouTube videos.
 
 
 
 
 Python
 
 
 
 249
 
 
 
 
 15
 
 
 
 
 
 
 
 Sublime Text plugin for programming contests - loads codeforces problems right into your text editor with a single button. 
 
 
 
 
 Python
 
 
 
 38
 
 
 
 
 4
 
 
 
 
 
 
 
 Chrome extension to overlay a (super adorable) rubber duck, as a virtual companion during rubber duck debugging. 
 
 
 
 
 JavaScript
 
 
 
 80
 
 
 
 
 9
 
 
 
 
 
 
 
 Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)
 
 
 
 
 C++
 
 
 
 4.2k
 
 
 
 
 988
 
 
 
 
 
 
 
 Markov model manually implemented that predicts words probabilistically based on historic data.
 
 
 
 
 Python
 
 
 
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RameshAditya - Overview
https://github.com/RameshAditya
https://scholar.google.be/citations?user=60K82BkAAAAJ&hl=cs
Aditya A. Ramesh
2016-10-20
None
<div><div><table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:k_IJM867U9cC">Mindstorms in natural language-based societies of mind</a><p>M Zhuge, H Liu, F Faccio, DR Ashley, R Csordás, A Gopalakrishnan, ...</p><p>arXiv preprint arXiv:2305.17066</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=14792193560306780956">32</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:u5HHmVD_uO8C">Automatic seizure detection by modified line length and Mahalanobis distance function</a><p>A Pathak, A Ramesh, A Mitra, K Majumdar</p><p>Biomedical Signal Processing and Control 44, 279-287</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=168031087220795056">14</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:TQgYirikUcIC">The Benefits of Model-Based Generalization in Reinforcement Learning</a><p>K Young, A Ramesh, L Kirsch, J Schmidhuber</p><p>Proceedings of the 40th International Conference on Machine Learning</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=12682449560348364791">13</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:IWHjjKOFINEC">Goal-conditioned generators of deep policies</a><p>F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber</p><p>Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7503-7511</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=1455336595930071883">11</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:hC7cP41nSMkC">General policy evaluation and improvement by learning to identify few but crucial states</a><p>F Faccio, A Ramesh, V Herrmann, J Harb, J Schmidhuber</p><p>arXiv preprint arXiv:2207.01566</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=13842577082735883972">10</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:M3NEmzRMIkIC">Exploring through random curiosity with general value functions</a><p>A Ramesh, L Kirsch, S van Steenkiste, J Schmidhuber</p><p>Advances in Neural Information Processing Systems 35, 18733-18748</p></td><td><a href="https://scholar.google.be/scholar?oi=bibs&amp;hl=cs&amp;cites=11196854224809842517">7</a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:GnPB-g6toBAC">Efficient Value Propagation with the Compositional Optimality Equation</a><p>P Piekos, A Ramesh, F Faccio, J Schmidhuber</p><p>GCRL Workshop, OpenReview. net</p></td><td><a href=""></a></td><td></td></tr><tr><td><a href="https://scholar.google.be/citations?view_op=view_citation&amp;hl=cs&amp;user=60K82BkAAAAJ&amp;citation_for_view=60K82BkAAAAJ:r0BpntZqJG4C">Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits</a><p>A Ramesh, P Rauber, M Conserva, J Schmidhuber</p><p>Neural Computation 34 (11), 2232-2272</p></td><td><a href=""></a></td><td></td></tr></tbody></table><p>Systém momentálně nemůže danou operaci provést. Zkuste to znovu později.</p></div></div>||||I|||| Citace za rok

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 Spoluautoři

 * Juergen Schmidhuber King Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of Lugano E-mailová adresa ověřena na: kaust.edu.sa
 * Louis Kirsch The Swiss AI Lab IDSIA E-mailová adresa ověřena na: idsia.ch
 * Francesco Faccio The Swiss AI Lab IDSIA / USI & SUPSI, KAUST AI Initiative E-mailová adresa ověřena na: idsia.ch
 * Vincent Herrmann IDSIA E-mailová adresa ověřena na: idsia.ch
 * Kaushik Kumar Majumdar Professor at Indian Statistical Institute, Bangalore Centre E-mailová adresa ověřena na: isibang.ac.in
 * Kenny Young University of Alberta E-mailová adresa ověřena na: ualberta.ca
 * Sjoerd van Steenkiste Research Scientist at Google Research E-mailová adresa ověřena na: google.com
 * Michelangelo Conserva Queen Mary University of London E-mailová adresa ověřena na: qmul.ac.uk
 * Piotr Piękos KAUST E-mailová adresa ověřena na: kaust.edu.sa
 Sledovat
 Aditya A. Ramesh
 PhD student at the Swiss AI Lab (IDSIA)
 E-mailová adresa ověřena na: idsia.ch - Domovská stránka
 Artificial IntelligenceReinforcement LearningDeep Learning
 ČlánkyCitaceVeřejný přístupSpoluautoři
  
 Název  Citace 
 Seřadit  Citace Rok 
 Seřadit podle citacíSeřadit podle rokuSeřadit podle názvu 
 Mindstorms in natural language-based societies of mind 
 M Zhuge, H Liu, F Faccio, DR Ashley, R Csordás, A Gopalakrishnan, ... 32 2023
 arXiv preprint arXiv:2305.17066 , 2023 
 Automatic seizure detection by modified line length and Mahalanobis distance function 
 A Pathak, A Ramesh, A Mitra, K Majumdar 14 2018
 Biomedical Signal Processing and Control 44, 279-287 , 2018 
 The Benefits of Model-Based Generalization in Reinforcement Learning 
 K Young, A Ramesh, L Kirsch, J Schmidhuber 13 2023
 Proceedings of the 40th International Conference on Machine Learning , 2023 
 Goal-conditioned generators of deep policies 
 F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber 11 2023
 Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7503-7511 , 2023 
 General policy evaluation and improvement by learning to identify few but crucial states 
 F Faccio, A Ramesh, V Herrmann, J Harb, J Schmidhuber 10 2022
 arXiv preprint arXiv:2207.01566 , 2022 
 Exploring through random curiosity with general value functions 
 A Ramesh, L Kirsch, S van Steenkiste, J Schmidhuber 7 2022
 Advances in Neural Information Processing Systems 35, 18733-18748 , 2022 
 Efficient Value Propagation with the Compositional Optimality Equation 
 P Piekos, A Ramesh, F Faccio, J Schmidhuber 2023
 GCRL Workshop, OpenReview. net , 2023  
 Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits 
 A Ramesh, P Rauber, M Conserva, J Schmidhuber 2022
 Neural Computation 34 (11), 2232-2272 , 2022 
 
 Systém momentálně nemůže danou operaci provést. Zkuste to znovu později.
 Články 1–8
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Mindstorms in natural language-based societies of mind M Zhuge, H Liu, F Faccio, DR Ashley, R Csordás, A Gopalakrishnan, ... arXiv preprint arXiv:2305.17066   32       Automatic seizure detection by modified line length and Mahalanobis distance function A Pathak, A Ramesh, A Mitra, K Majumdar Biomedical Signal Processing and Control 44, 279-287   14       The Benefits of Model-Based Generalization in Reinforcement Learning K Young, A Ramesh, L Kirsch, J Schmidhuber Proceedings of the 40th International Conference on Machine Learning   13       Goal-conditioned generators of deep policies F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7503-7511   11       General policy evaluation and improvement by learning to identify few but crucial states F Faccio, A Ramesh, V Herrmann, J Harb, J Schmidhuber arXiv preprint arXiv:2207.01566   10       Exploring through random curiosity with general value functions A Ramesh, L Kirsch, S van Steenkiste, J Schmidhuber Advances in Neural Information Processing Systems 35, 18733-18748   7       Efficient Value Propagation with the Compositional Optimality Equation P Piekos, A Ramesh, F Faccio, J Schmidhuber GCRL Workshop, OpenReview. net           Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits A Ramesh, P Rauber, M Conserva, J Schmidhuber Neural Computation 34 (11), 2232-2272           Systém momentálně nemůže danou operaci provést. Zkuste to znovu později.
Aditya A. Ramesh
https://scholar.google.be/citations?user=60K82BkAAAAJ&hl=cs
https://adityaramesh.in/cv/
Short CV
2012-03-01
None
<div><div><h2>Education</h2><ul><li>B.S. in GitHub, GitHub University, 2012</li><li>M.S. in Jekyll, GitHub University, 2014</li><li>Ph.D. in Artificial Intelligence, Swiss AI Lab (IDSIA), 2020-</li></ul><h2>Work experience</h2><ul><li>Summer 2015: Research Assistant<ul><li>Github University</li><li>Duties included: Tagging issues</li><li>Supervisor: Professor Git</li></ul></li><li>Fall 2015: Research Assistant<ul><li>Github University</li><li>Duties included: Merging pull requests</li><li>Supervisor: Professor Hub</li></ul></li></ul><h2>Publications</h2><ul></ul><h2>Presentations</h2><ul></ul><h2>Teaching</h2><ul></ul><h2>Reviewing</h2><ul><li><a href="https://sites.google.com/view/neverendingrl">A Roadmoad to Never-Ending Reinforcement Learning Workshop at International Conference on Learning Representations (ICLR) 2021</a></li><li>Sub-reviewer at International Conference on Learning Representations (ICLR) 2021</li><li>Sub-reviewer at Conference on Neural Information Processing Systems (NeurIPS) 2020</li></ul></div></div>||||I|||| * Aditya A. Ramesh

 Aditya A. Ramesh

 Follow
 * Lugano, Switzerland
 * Email
 * Github
 * Google Scholar

 Short CV

 Education

 * B.S. in GitHub, GitHub University, 2012
 * M.S. in Jekyll, GitHub University, 2014
 * Ph.D. in Artificial Intelligence, Swiss AI Lab (IDSIA), 2020-

 Work experience

 * Summer 2015: Research Assistant
 + Github University
 + Duties included: Tagging issues
 + Supervisor: Professor Git
 * Fall 2015: Research Assistant
 + Github University
 + Duties included: Merging pull requests
 + Supervisor: Professor Hub

 Publications

 * Automatic Seizure Detection by Modified Line Length and Mahalanobis Distance Function.

 * General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States

 * Goal-Conditioned Generators of Deep Policies

 * Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits

 * Exploring through Random Curiosity with General Value Functions

 Presentations

 * Talk 1 on Relevant Topic in Your Field

 March 01, 2012

 Talk at UC San Francisco, Department of Testing, San Francisco, California

 * Tutorial 1 on Relevant Topic in Your Field

 March 01, 2013

 Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA

 * Talk 2 on Relevant Topic in Your Field

 February 01, 2014

 Talk at London School of Testing, London, UK

 * Conference Proceeding talk 3 on Relevant Topic in Your Field

 March 01, 2014

 Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA

 Teaching

 * Teaching experience 1

 * Teaching experience 2

 Reviewing

 * A Roadmoad to Never-Ending Reinforcement Learning Workshop at International Conference on Learning Representations (ICLR) 2021
 * Sub-reviewer at International Conference on Learning Representations (ICLR) 2021
 * Sub-reviewer at Conference on Neural Information Processing Systems (NeurIPS) 2020
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 * Follow:
 * GitHub
 * Feed
 © 2024 Aditya A. Ramesh. Powered by Jekyll & AcademicPages, a fork of Minimal Mistakes.
Education  B.S. in GitHub, GitHub University, 2012 M.S. in Jekyll, GitHub University, 2014 Ph.D. in Artificial Intelligence, Swiss AI Lab (IDSIA), 2020-  Work experience  Summer 2015: Research Assistant Github University Duties included: Tagging issues Supervisor: Professor Git   Fall 2015: Research Assistant Github University Duties included: Merging pull requests Supervisor: Professor Hub    Publications   Presentations   Teaching   Reviewing   A Roadmoad to Never-Ending Reinforcement Learning Workshop at International Conference on Learning Representations (ICLR) 2021  Sub-reviewer at International Conference on Learning Representations (ICLR) 2021 Sub-reviewer at Conference on Neural Information Processing Systems (NeurIPS) 2020
Short CV
https://adityaramesh.in/cv/