A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: obtaining training examples, each training example comprising acquired data and a machine learning task output label; training a neural network using one or more error terms obtained from a loss function to update a set of parameters of the neural network, where the neural network comprises an encoder portion and a task portion, and where the loss function comprises: a first loss describing an accuracy of a machine learning task output predicted by the neural network in comparison to the machine task output label of the training example, and a second loss describing an encoding efficiency of compressed codes generated from a compressed representation of the acquired data of the training example output by an intermediate layer of the neural network where the encoder portion terminates and before the decoder portion; and storing the set of parameters of the neural network on a computer readable medium. 2. The method of claim 1, wherein training the neural network comprises: applying the acquired data of the training example as input to the encoder portion of the neural network to obtain a compressed representation of the acquired data; and determining the second loss by comparing a codelength of compressed codes generated from the compressed representation to a target codelength. 3. The method of claim 2, wherein the target codelength is set based on an amount of available bandwidth. 4. The method of claim 2, wherein training the neural network further comprises: applying the compressed representation as input to the task portion of the neural network to obtain a predicted machine learning task output; and determining the first loss by comparing the machine learning task output to the machine learning task output label of the training example. 5. The method of claim 1, wherein the neural network is trained to maximize the accuracy of the machine learning task output predicted by the neural network while ensuring that the compressed codes generated from the compressed representation does not exceed a threshold codelength that is dependent on available bandwidth. 6. The method of claim 1, wherein the neural network is trained to minimize a codelength of the compressed codes generated from the compressed representation while maintaining the accuracy of the machine learning task output predicted by the neural network at a minimum accuracy threshold. 7. The method of claim 1, wherein the neural network further comprises one or more additional encoder portions, wherein nodes in an output layer of each of the one or more encoder portions serve as nodes in an input layer of the task portion of the neural network. 8. The method of claim 7, wherein for each of the additional encoder portions of the neural network, the additional encoder portion is trained using: the first loss describing the accuracy of the machine learning task output, and an additional loss describing an encoding efficiency of compressed codes generated from a representation outputted by the additional encoder portion. 9. The method of claim 1, wherein a machine learning task of the machine learning task output predicted by the neural network is one of a classification task, regression task, clustering task, density estimation task, dimensionality reduction task, or multivariate querying task. 10. The method of claim 9, wherein the classification task is one of an object detection or object recognition task. 11. A system comprising: a data compression system comprising: a data acquisition module coupled to acquire data; an encoder portion of a neural network coupled to the data acquisition module to encode the acquired data into a compressed representation, wherein the neural network is trained using: a first loss describing an accuracy of a machine learning task output predicted by the neural network in comparison to the machine task output label; and a second loss describing an encoding efficiency of compressed codes generated from a compressed representation of the acquired data output by an intermediate layer of the neural network, wherein the neural network comprises the encoder portion and further comprises a task portion, the encoder portion terminating in the intermediate layer of the neural network; and a coding module coupled to the encoder portion of the neural network to encode the compressed representation into compressed codes; and a machine learning task system comprising: a decoding module coupled to decode the compressed codes to obtain the compressed representation; and a task portion of the neural network coupled to the decoding module to predict a machine learning task output from the compressed representation. 12. The system of claim 11, wherein training the neural network comprises: applying the acquired data as input to the encoder portion of the neural network to obtain the compressed representation of the acquired data; and determining the second loss by comparing a codelength of compressed codes generated from the compressed representation to a target codelength. 13. The system of claim 12, wherein the target codelength is set based on an amount of available bandwidth. 14. The system of claim 12, wherein training the neural network further comprises: applying the compressed representation as input to the task portion of the neural network to obtain a predicted machine learning task output; and determining the first loss by comparing the machine learning task output to a machine learning task output label. 15. The system of claim 11, wherein the neural network is trained to maximize the accuracy of the machine learning task output predicted by the neural network while ensuring that the compressed codes generated from the compressed representation does not exceed a threshold codelength that is dependent on available bandwidth. 16. The system of claim 11, wherein the neural network is trained to minimize a codelength of the compressed codes generated from the compressed representation while maintaining the accuracy of the machine learning task output predicted by the neural network at a minimum accuracy threshold. 17. The system of claim 11, further comprising one or more additional data compression systems that each comprise an additional encoder portion of the neural network, wherein nodes in an output layer of each of the one or more encoder portions serve as nodes in an input layer of the task portion of the neural network. 18. The system of claim 17, wherein for each of the additional encoder portions of the neural network, the additional encoder portion is trained using: the first loss describing the accuracy of the machine learning task output, and an additional loss describing an encoding efficiency of compressed codes generated from a representation outputted by the additional encoder portion. 19. The system of claim 11, wherein a machine learning task of the machine learning task output predicted by the neural network is one of a classification task, regression task, clustering task, density estimation task, dimensionality reduction task, or multivariate querying task. 20. The system of claim 19, wherein the classification task is one of an object detection or object recognition task. 21. An article of manufacture comprising parameters of an encoder portion of a neural network stored on a computer readable storage medium, wherein the encoder portion is produced by a process comprising: obtain training examples, each training example comprising acquired data and a machine task output label; training a neural network using one or more error terms obtained from a loss function to update a set of parameters of the neural network, the loss function comprising: a first loss describing an accuracy of a machine learning task output predicted by the neural network in comparison to the machine task output label of the training example; and a second loss describing an encoding efficiency of compressed codes generated from a compressed representation of the acquired data of the training example output by an intermediate layer of the neural network, wherein the neural network comprises the encoder portion and a task portion, the encoder portion terminating in the intermediate layer of the neural network; and storing parameters of the encoder portion of the neural network on the computer readable storage medium. 22. The article of manufacture of claim 21, wherein training the parameters of the neural network comprises: applying the acquired data of the training example as input to the encoder portion of the neural network to obtain a compressed representation of the acquired data; and determining the second loss by comparing a codelength of compressed codes generated from the compressed representation to a target codelength. 23. The article of manufacture of claim 22, wherein the target codelength is set based on an amount of available bandwidth. 24. The article of manufacture of claim 22, wherein training the parameters of the neural network further comprises: applying the compressed representation as input to the task portion of the neural network to obtain a predicted machine learning task output; and determining the first loss by comparing the machine learning task output to the machine learning task output label of the training example. 25. The article of manufacture of claim 21, wherein the neural network is trained to maximize the accuracy of the machine learning task output predicted by the neural network while ensuring that the compressed codes generated from the compressed representation does not exceed a threshold codelength that is dependent on available bandwidth. 26. The article of manufacture of claim 21, wherein the neural network is trained to minimize a codelength of the compressed codes generated from the compressed representation while maintaining the accuracy of the machine learning task output predicted by the neural network at a minimum accuracy threshold. 27. The article of manufacture of claim 21, wherein the neural network further comprises one or more additional encoder portions, wherein nodes in an output layer of each of the one or more encoder portions serve as nodes in an input layer of the task portion of the neural network. 28. The article of manufacture of claim 27, wherein for each of the additional encoder portions of the neural network, the additional encoder portion is trained using: the first loss describing the accuracy of the machine learning task output, and an additional loss describing an encoding efficiency of compressed codes generated from a representation outputted by the additional encoder portion. 29. The method of claim 21, wherein a machine learning task of the machine learning task output predicted by the neural network is one of a classification task, regression task, clustering task, density estimation task, dimensionality reduction task, or multivariate querying task. 30. The article of manufacture of claim 29, wherein the classification task is one of an object detection or object recognition task. 31. An article of manufacture comprising parameters of a task portion of a neural network stored on a computer readable storage medium, wherein the task portion is produced by a process comprising: obtain training examples, each training example comprising acquired data and a machine task output label; training the neural network using one or more error terms obtained from a loss function to update a set of parameters of the neural network, the loss function comprising: a first loss describing an accuracy of a machine learning task output predicted by the neural network in comparison to the machine task output label of the training example; and a second loss describing an encoding efficiency of compressed codes generated from a compressed representation of the acquired data of the training example output by an intermediate layer of the neural network, wherein the neural network comprises an encoder portion and the task portion, the encoder portion terminating in the intermediate layer of the neural network; and storing parameters of the task portion of the neural network on the computer readable storage medium. 32. The article of manufacture of claim 31, wherein training the parameters of the neural network comprises: applying the acquired data of the training example as input to the encoder portion of the neural network to obtain a compressed representation of the acquired data; and determining the second loss by comparing a codelength of compressed codes generated from the compressed representation to a target codelength. 33. The article of manufacture of claim 32, wherein the target codelength is set based on an amount of available bandwidth. 34. The article of manufacture of claim 32, wherein training the parameters of the neural network further comprises: applying the compressed representation as input to the task portion of the neural network to obtain a predicted machine learning task output; and determining the first loss by comparing the machine learning task output to the machine learning task output label of the training example. 35. The article of manufacture of claim 31, wherein the neural network is trained to maximize the accuracy of the machine learning task output predicted by the neural network while ensuring that the compressed codes generated from the compressed representation does not exceed a threshold codelength that is dependent on available bandwidth. 36. The article of manufacture of claim 31, wherein the neural network is trained to minimize a codelength of the compressed codes generated from the compressed representation while maintaining the accuracy of the machine learning task output predicted by the neural network at a minimum accuracy threshold. 37. The article of manufacture of claim 31, wherein the neural network further comprises one or more additional encoder portions, wherein nodes in an output layer of each of the one or more encoder portions serve as nodes in an input layer of the task portion of the neural network. 38. The article of manufacture of claim 37, wherein for each of the additional encoder portions of the neural network, the additional encoder portion is trained using: the first loss describing the accuracy of the machine learning task output, and an additional loss describing an encoding efficiency of compressed codes generated from a representation outputted by the additional encoder portion. 39. The method of claim 31, wherein a machine learning task of the machine learning task output predicted by the neural network is one of a classification task, regression task, clustering task, density estimation task, dimensionality reduction task, or multivariate querying task. 40. The article of manufacture of claim 39, wherein the classification task is one of an object detection or object recognition task.