They involve probing large address ranges ( IPv4 or parts of IPv6 ) for specific ports or protocols . Besides their primary use for probing ( e.g. , studying protocol adoption ) , we show that — at the same time — they provide valuable insights into the Internet control plane informed by ICMP responses to these probes — a currently unexplored secondary use . We collect one week of ICMP responses ( 637.50 M messages ) to several Internet-wide ZMap scans covering multiple TCP and UDP ports as well as DNS-based scans covering > 50 % of the domain name space . Our motivation in this paper is to balance the elimination of delay jitter and the decrease of end-to-end delay . We propose a novel adaptive playback buffer ( APB ) based on the probing scheme . By utilizing the probing scheme , instantaneous network situations are collected and then using with the delay margin and the delay jitter margin , the playback buffer is adaptively adjusted to represent the continuous and real-time streaming media at the receiver . This paper presents a new method for estimating of traffic in mobile ad hoc by using probe packets . Probing based approaches involves sending test transactions over the network to monitor the path and performance of network . These test transactions are called probes . The scan-chain is still accessible , yet there is no key to be attacked . We show that the attacker can use the scan chain to reveal information that can be used to improve other physical attacks , such side-channel analysis , fault analysis and probing analysis . We introduce a new probing problem : what is the minimum number of cameras at fixed positions necessary and sufficient to reconstruct any strictly convex polygon contained in a disk of radius 1 if cameras only see the silhouette of the polygon ? The optimal number only depends on the largest angle ? However , to what extent is the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work , we examine this probing paradigm through a case-study in Natural Language Inference , showing that models learn to encode linguistic properties even when not needed for a task . We identify that pre-trained word embeddings play a considerable role in encoding these properties rather than the training task itself , highlighting the importance of careful controls when designing probing experiments . In this paper , we propose a fast probing detection technique for intrusion detection . In the past , probing detection was performed by analyzing all collected traffic characteristics and by supervised learning . In the proposed method , a normal traffic is classified through unsupervised learning and intrusion detection for probing attack is not performed for that traffic . Two different approaches are presented for implementing the relay-based forwarding . In the first approach , a probing protocol is used to identify the relay node and the routing metrics of the superedges . Tunneling and packet encapsulation are then used to forward packets from the source node to the relay node and then from the relay node to the destination node . Therefore , the primary challenge for a small cell deployment becomes the detection of such hotspots in a considered LTE macro cell network . We present a novel method allowing for hotspot detection by a probing related procedure that is based on LTE standard compliant signaling and ensures mobile user privacy protection by design . A large scale video dataset has been created to support this line of research . The second direction focuses on the development of explainable AI models , which are just beginning . Existing works focus on either the intrinsic approach , which designs self-explanatory models , or post-hoc approach , which constructs a second model to interpret the target model . Deep neural networks have achieved great success in many real-world applications , yet it remains unclear and difficult to explain their decision-making process to an enduser . In this paper , we address the explainable AI problem for deep neural networks with our proposed framework , named IASSA , which generates an importance map indicating how salient each pixel is for the models prediction with an iterative and adaptive sampling module . We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation , which can shift the saliency values guided by our long-range and parameter-free spatial attention module . Recent public calls for the development of explainable and verifiable AI led to a growing interest in formal verification and repair of machine-learned models . Despite the impressive progress that the learning community has made , models such as deep neural networks remain vulnerable to adversarial attacks , and their sheer size represents a major obstacle to formal analysis and implementation . Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users . A general framework for explaining any AI model is provided by the Shapley values that attribute the prediction output to the various model inputs ( " features " ) in a principled and model-agnostic way . The use of different artificial intelligence ( AI ) techniques for clutter signals identification in the context of radar based precipitation estimation is presented . The clutter signals considered are because of ground clutter , sea clutter and anomalous propagation whereas the explored AI techniques include the support vector machine ( SVM ) , the artificial neural network ( ANN ) , the decision tree ( DT ) , and the nearest neighbour ( NN ) systems . Eight different radar measurement combinations comprising of various polarimetric spectral signatures — the reflectivity ( ZH ) , differential reflectivity ( ZDR ) , differential propagation phase ( ΦDP ) , cross-correlation coefficient ( ρHV ) , velocity ( V ) and spectral width ( W ) from a C-band polarimetric radar are taken into account as input vectors to the AI systems . Our method corrects overconfident NN decisions , detects outlier points and learns to say ` ` I do n't know ' ' when uncertain about a critical point between the top two predictions . In addition , we provide a mechanism to quantify class distributions overlap in the decision manifold and investigate its implications in model interpretability . Our method is two-step : in the first step , the proposed method builds a class distribution using Kernel Activation Vectors ( kav ) extracted from the Network . Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions . Decision-trees are an interpretable modelling technique widely used in ecology , able to handle strongly nonlinear relationships with high order interactions and diverse variable types . Decision-trees recursively split the input space into two parts maximising child node homogeneity . Estimating the error variances of such a model , we are able to obtain samples from the posterior distribution of the signal-to-noise ratio for each voxel , which is used as a criterion for the detection of brain activity . The benefits of this approach are : ( i ) the reduced number of parameters , ( ii ) the model makes no assumptions about the stimulation paradigm , ( iii ) an interpretable model based approach , and ( iv ) flexibility . The performance of the proposed method is shown by simulations and further results are presented on the application of the model for the analysis of a real fMRI data set , in order to illustrate some practical issues and to compare with previously proposed techniques . We introduce a new sparse recovery paradigm , called NORMED PURSUITS , where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions . Synthetic and real data experiments illustrate that NORMED PURSUITS can significantly enhance the performance of both hard thresholding methods and convex solvers in sparse recovery . First , a reference model , possibly a black-box Bayesian predictive model compromising no accuracy , is constructed and fitted to the training data . Second , a proxy model from an interpretable model family that best mimics the predictive behaviour of the reference model is found by optimizing the interpretability utility function . The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family . The experiments used the Auto-WEKA tool to investigate this trade-off . We distinguish between white box ( interpretable ) model representations and two other types of model representations : black box ( non-interpretable ) and grey box ( partly interpretable ) . We consider as white box the models based on the following 6 interpretable knowledge representations : decision trees , If-Then classification rules , decision tables , Bayesian network classifiers , nearest neighbours and logistic regression . To understand changes in physical systems and facilitate decisions , explaining how model predictions are made is crucial . We use model-based interpretability , where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed . We use the port Hamiltonian ( p-H ) formalism to describe the basic constructs that contain physically interpretable processes commonly found in the behavior of physical systems . The AOP model interpreter can be integrated into generic modeling environment ( GME ) conveniently , so as to support the whole process of system development using AO . Furthermore , the weaving is postponed from model interpreting to AOP language interpreting , which focuses the model-designers ' attention on the construction of domain model itself and integrates the ability of AOP weaver in stock into the mapping procedure from AO model to AO application smoothly . Finally , several examples are described to illustrate the encapsulation of crosscutting concerns with the support of AOP model interpreter document : Image Reconstruction with Predictive Filter Flow We propose a simple , interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution . Given a corrupted input image , the model synthesizes a spatially varying linear filter which , when applied to the input image , reconstructs the desired output . There are several potential improvements and extensions to graph attention networks that could be addressed as future work , such as overcoming the practical problems described in subsection 2.2 to be able to handle larger batch sizes . A particularly interesting research direction would be taking advantage of the attention mechanism to perform a thorough analysis on the model interpretability . Moreover , extending the method to perform graph classification instead of node classification would also be relevant from the application perspective . Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks . Although gradient-based saliency maps are popular methods for deep learning interpretation , recent works show that they can be vulnerable to adversarial attacks . In this paper , we address this problem and provide a certifiable defense method for deep learning interpretation . Hardware-oriented approaches to accelerate deep neural network processing are very important for various embedded intelligent applications . This paper is a summary of our recent achievements for efficient neural network processing . Most interestingly , doing so shows that the k-means algorithm , a staple of " conventional ' " or " shallow ' " machine learning , can actually be seen as a special case of deep learning , contrary to the general perception that deep learning is a subset of machine learning . Doing so also automatically introduces yet another unsupervised learning technique into the arsenal of deep learning , which happens to be an example of interpretable deep neural networks as well . Finally , we also show how to utilize the powerful deep learning infrastructures with very little extra effort for adaptation . Automatic detection of AF could make a significant contribution to early diagnosis , control and prevention of chronic AF complications . In this paper , authors presented a novel algorithm through spectrogram and deep learning neural network analysis in detecting paroxysmal AF from image data segments . This method does not require the detection of P and/or R peaks which is a preprocessing step required by many existing algorithms . In other words , the neural network should provide human-understandable justifications for its output leading to insights about the inner workings . We call such models as interpretable deep networks . Interpretability is not a monolithic notion . Due to the impact they pose on influencing the user communities , understanding the bot behaviour is important . In this paper , we employ deep neural network analysis on temporal data of bot accounts and have identified the role of temporal activity in bot detection . The bidirectional LSTM network is used for studying the temporal patterns of Twitter bots and its behavioural pattern . This error distribution depends both on the current state of the object ( e.g. distance from the sensor ) and its past residuals . We assume the error distribution is conditionally Gaussian , and we use a deep neural neural network ( DNN ) to map the object states and past residuals to the distribution parameters ( mean and variance ) . Our conditional model perturbs the dynamic objects ’ states ( position , velocities , orientations , and shape ) and produces smoother trajectories which look similar to the real data . We demonstrate that both methods reduce to finding optimal monotone functions , which we do so by performing Bayesian optimization with a Gaussian process ( GP ) prior . We conduct experiments on computer Go , where the returns are given by a deep value neural network , and show that our proposed framework outperforms previous methods . Our findings also suggest that supervised neural ranking models can greatly benefit from pre - training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models . KEYWORDS Ranking model , weak supervision , deep neural network , deep learning , ad - hoc retrieval compat=1.11 ternary compat Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics . The desired neural output is achieved through the use of complex neural models with multiple parameters which need to be tuned - a time consuming and difficult task . Understanding neuron connections is a great challenge , which is needed to solve many important problems in neurobiology and neuroengineering for recreation of brain functions and efficient biorobotics . In particular , a design of an optical synapse capable to communicate with neuron spike sequences would be crucial to improve the functionality of neuromimmetic networks . [ 17th neuron ] subsubsection : Understanding Behaviors of Neurons in C - LSTMs To get an intuitive understanding of how the C - LSTMs work on this problem , we examined the neuron activations in the last aggregation layer while evaluating the test set using TC - LSTMs .
They involve probing large address ranges ( IPv4 or parts of IPv6 ) for specific ports or protocols . Besides their primary use for probing ( e.g. , studying protocol adoption ) , we show that — at the same time — they provide valuable insights into the Internet control plane informed by ICMP responses to these probes — a currently unexplored secondary use . We collect one week of ICMP responses ( 637.50 M messages ) to several Internet-wide ZMap scans covering multiple TCP and UDP ports as well as DNS-based scans covering > 50 % of the domain name space .
Our motivation in this paper is to balance the elimination of delay jitter and the decrease of end-to-end delay . We propose a novel adaptive playback buffer ( APB ) based on the probing scheme . By utilizing the probing scheme , instantaneous network situations are collected and then using with the delay margin and the delay jitter margin , the playback buffer is adaptively adjusted to represent the continuous and real-time streaming media at the receiver .
This paper presents a new method for estimating of traffic in mobile ad hoc by using probe packets . Probing based approaches involves sending test transactions over the network to monitor the path and performance of network . These test transactions are called probes .
The scan-chain is still accessible , yet there is no key to be attacked . We show that the attacker can use the scan chain to reveal information that can be used to improve other physical attacks , such side-channel analysis , fault analysis and probing analysis .
We introduce a new probing problem : what is the minimum number of cameras at fixed positions necessary and sufficient to reconstruct any strictly convex polygon contained in a disk of radius 1 if cameras only see the silhouette of the polygon ? The optimal number only depends on the largest angle ?
However , to what extent is the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work , we examine this probing paradigm through a case-study in Natural Language Inference , showing that models learn to encode linguistic properties even when not needed for a task . We identify that pre-trained word embeddings play a considerable role in encoding these properties rather than the training task itself , highlighting the importance of careful controls when designing probing experiments .
In this paper , we propose a fast probing detection technique for intrusion detection . In the past , probing detection was performed by analyzing all collected traffic characteristics and by supervised learning . In the proposed method , a normal traffic is classified through unsupervised learning and intrusion detection for probing attack is not performed for that traffic .
Two different approaches are presented for implementing the relay-based forwarding . In the first approach , a probing protocol is used to identify the relay node and the routing metrics of the superedges . Tunneling and packet encapsulation are then used to forward packets from the source node to the relay node and then from the relay node to the destination node .
Therefore , the primary challenge for a small cell deployment becomes the detection of such hotspots in a considered LTE macro cell network . We present a novel method allowing for hotspot detection by a probing related procedure that is based on LTE standard compliant signaling and ensures mobile user privacy protection by design .
A large scale video dataset has been created to support this line of research . The second direction focuses on the development of explainable AI models , which are just beginning . Existing works focus on either the intrinsic approach , which designs self-explanatory models , or post-hoc approach , which constructs a second model to interpret the target model .
Deep neural networks have achieved great success in many real-world applications , yet it remains unclear and difficult to explain their decision-making process to an enduser . In this paper , we address the explainable AI problem for deep neural networks with our proposed framework , named IASSA , which generates an importance map indicating how salient each pixel is for the models prediction with an iterative and adaptive sampling module . We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation , which can shift the saliency values guided by our long-range and parameter-free spatial attention module .
Recent public calls for the development of explainable and verifiable AI led to a growing interest in formal verification and repair of machine-learned models . Despite the impressive progress that the learning community has made , models such as deep neural networks remain vulnerable to adversarial attacks , and their sheer size represents a major obstacle to formal analysis and implementation .
Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users . A general framework for explaining any AI model is provided by the Shapley values that attribute the prediction output to the various model inputs ( " features " ) in a principled and model-agnostic way .
The use of different artificial intelligence ( AI ) techniques for clutter signals identification in the context of radar based precipitation estimation is presented . The clutter signals considered are because of ground clutter , sea clutter and anomalous propagation whereas the explored AI techniques include the support vector machine ( SVM ) , the artificial neural network ( ANN ) , the decision tree ( DT ) , and the nearest neighbour ( NN ) systems . Eight different radar measurement combinations comprising of various polarimetric spectral signatures — the reflectivity ( ZH ) , differential reflectivity ( ZDR ) , differential propagation phase ( ΦDP ) , cross-correlation coefficient ( ρHV ) , velocity ( V ) and spectral width ( W ) from a C-band polarimetric radar are taken into account as input vectors to the AI systems .
Our method corrects overconfident NN decisions , detects outlier points and learns to say ` ` I do n't know ' ' when uncertain about a critical point between the top two predictions . In addition , we provide a mechanism to quantify class distributions overlap in the decision manifold and investigate its implications in model interpretability . Our method is two-step : in the first step , the proposed method builds a class distribution using Kernel Activation Vectors ( kav ) extracted from the Network .
Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions . Decision-trees are an interpretable modelling technique widely used in ecology , able to handle strongly nonlinear relationships with high order interactions and diverse variable types . Decision-trees recursively split the input space into two parts maximising child node homogeneity .
Estimating the error variances of such a model , we are able to obtain samples from the posterior distribution of the signal-to-noise ratio for each voxel , which is used as a criterion for the detection of brain activity . The benefits of this approach are : ( i ) the reduced number of parameters , ( ii ) the model makes no assumptions about the stimulation paradigm , ( iii ) an interpretable model based approach , and ( iv ) flexibility . The performance of the proposed method is shown by simulations and further results are presented on the application of the model for the analysis of a real fMRI data set , in order to illustrate some practical issues and to compare with previously proposed techniques .
We introduce a new sparse recovery paradigm , called NORMED PURSUITS , where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions . Synthetic and real data experiments illustrate that NORMED PURSUITS can significantly enhance the performance of both hard thresholding methods and convex solvers in sparse recovery .
First , a reference model , possibly a black-box Bayesian predictive model compromising no accuracy , is constructed and fitted to the training data . Second , a proxy model from an interpretable model family that best mimics the predictive behaviour of the reference model is found by optimizing the interpretability utility function . The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family .
The experiments used the Auto-WEKA tool to investigate this trade-off . We distinguish between white box ( interpretable ) model representations and two other types of model representations : black box ( non-interpretable ) and grey box ( partly interpretable ) . We consider as white box the models based on the following 6 interpretable knowledge representations : decision trees , If-Then classification rules , decision tables , Bayesian network classifiers , nearest neighbours and logistic regression .
To understand changes in physical systems and facilitate decisions , explaining how model predictions are made is crucial . We use model-based interpretability , where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed . We use the port Hamiltonian ( p-H ) formalism to describe the basic constructs that contain physically interpretable processes commonly found in the behavior of physical systems .
The AOP model interpreter can be integrated into generic modeling environment ( GME ) conveniently , so as to support the whole process of system development using AO . Furthermore , the weaving is postponed from model interpreting to AOP language interpreting , which focuses the model-designers ' attention on the construction of domain model itself and integrates the ability of AOP weaver in stock into the mapping procedure from AO model to AO application smoothly . Finally , several examples are described to illustrate the encapsulation of crosscutting concerns with the support of AOP model interpreter
document : Image Reconstruction with Predictive Filter Flow We propose a simple , interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution . Given a corrupted input image , the model synthesizes a spatially varying linear filter which , when applied to the input image , reconstructs the desired output .
There are several potential improvements and extensions to graph attention networks that could be addressed as future work , such as overcoming the practical problems described in subsection 2.2 to be able to handle larger batch sizes . A particularly interesting research direction would be taking advantage of the attention mechanism to perform a thorough analysis on the model interpretability . Moreover , extending the method to perform graph classification instead of node classification would also be relevant from the application perspective .
Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks . Although gradient-based saliency maps are popular methods for deep learning interpretation , recent works show that they can be vulnerable to adversarial attacks . In this paper , we address this problem and provide a certifiable defense method for deep learning interpretation .
Hardware-oriented approaches to accelerate deep neural network processing are very important for various embedded intelligent applications . This paper is a summary of our recent achievements for efficient neural network processing .
Most interestingly , doing so shows that the k-means algorithm , a staple of " conventional ' " or " shallow ' " machine learning , can actually be seen as a special case of deep learning , contrary to the general perception that deep learning is a subset of machine learning . Doing so also automatically introduces yet another unsupervised learning technique into the arsenal of deep learning , which happens to be an example of interpretable deep neural networks as well . Finally , we also show how to utilize the powerful deep learning infrastructures with very little extra effort for adaptation .
Automatic detection of AF could make a significant contribution to early diagnosis , control and prevention of chronic AF complications . In this paper , authors presented a novel algorithm through spectrogram and deep learning neural network analysis in detecting paroxysmal AF from image data segments . This method does not require the detection of P and/or R peaks which is a preprocessing step required by many existing algorithms .
In other words , the neural network should provide human-understandable justifications for its output leading to insights about the inner workings . We call such models as interpretable deep networks . Interpretability is not a monolithic notion .
Due to the impact they pose on influencing the user communities , understanding the bot behaviour is important . In this paper , we employ deep neural network analysis on temporal data of bot accounts and have identified the role of temporal activity in bot detection . The bidirectional LSTM network is used for studying the temporal patterns of Twitter bots and its behavioural pattern .
This error distribution depends both on the current state of the object ( e.g. distance from the sensor ) and its past residuals . We assume the error distribution is conditionally Gaussian , and we use a deep neural neural network ( DNN ) to map the object states and past residuals to the distribution parameters ( mean and variance ) . Our conditional model perturbs the dynamic objects ’ states ( position , velocities , orientations , and shape ) and produces smoother trajectories which look similar to the real data .
We demonstrate that both methods reduce to finding optimal monotone functions , which we do so by performing Bayesian optimization with a Gaussian process ( GP ) prior . We conduct experiments on computer Go , where the returns are given by a deep value neural network , and show that our proposed framework outperforms previous methods .
Our findings also suggest that supervised neural ranking models can greatly benefit from pre - training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models . KEYWORDS Ranking model , weak supervision , deep neural network , deep learning , ad - hoc retrieval compat=1.11 ternary compat
Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics . The desired neural output is achieved through the use of complex neural models with multiple parameters which need to be tuned - a time consuming and difficult task .
Understanding neuron connections is a great challenge , which is needed to solve many important problems in neurobiology and neuroengineering for recreation of brain functions and efficient biorobotics . In particular , a design of an optical synapse capable to communicate with neuron spike sequences would be crucial to improve the functionality of neuromimmetic networks .
[ 17th neuron ] subsubsection : Understanding Behaviors of Neurons in C - LSTMs To get an intuitive understanding of how the C - LSTMs work on this problem , we examined the neuron activations in the last aggregation layer while evaluating the test set using TC - LSTMs .
Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane Adaptive playback buffer for wireless streaming media Traffic Estimation in Mobile Ad Hoc Network Using Probe Packets On the Use of Scan Chain to Improve Physical Attacks (Extended Abstract) On the Camera Placement Problem Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? A Fast Probing Detection Method using Hybrid Machine Learning Algorithms Algorithms and protocols for stateless constrained-based routing Mobile User Hotspot Detection in LTE Networks by Moving Pseudo Pico Cells Keynote: Towards Explainability in AI and Multimedia Research Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations Verification and Repair of Neural Networks: a Progress Report on Convolutional Models Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Artificial intelligence techniques for clutter identification with polarimetric radar signatures Fine-grained Uncertainty Modeling in Neural Networks Comparing four methods for decision-tree induction: A case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio and Madeira, 2004) Brain activity detection by estimating the signal-to-noise ratio of fMRI time series using dynamic linear models Hard thresholding with norm constraints Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach Automated Machine Learning for Studying the Trade-Off Between Predictive Accuracy and Interpretability Interpretable machine learning models: a physics-based view Mapping Aspect-Oriented Domain-Specific Model to Code for Real Time System Image Reconstruction with Predictive Filter Flow Graph Attention Networks Certifiably Robust Interpretation in Deep Learning Accelerating deep learning by binarized hardware Shallow Deep Learning: Embedding Verbatim K-Means in Deep Neural Networks Spectrogram and Deep Neural Network Analysis in Detecting Paroxysmal Atrial Fibrillation with Bottleneck Layers and Cross Entropy Approach Interpretability of deep learning models: A survey of results Deep Temporal Analysis of Twitter Bots Learning Conditional Error Model for Simulated Time-Series Data Bayesian optimization for backpropagation in Monte-Carlo tree search Neural Ranking Models with Weak Supervision Maximum likelihood parameter estimation of a spiking silicon neuron Optical fiber synaptic sensor Modelling Interaction of Sentence Pair with Coupled-LSTMs
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