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09/26/2021
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
We propose Hypernetwork Kalman Filter (HKF) for tracking applications wi...
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06/15/2021
A White Paper on Neural Network Quantization
While neural networks have advanced the frontiers in many applications, ...
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10/20/2020
Invertible Low-Divergence Coding
Several applications in communication, control, and learning require app...
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05/14/2020
Bayesian Bits: Unifying Quantization and Pruning
We introduce Bayesian Bits, a practical method for joint mixed precision...
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04/22/2020
Up or Down? Adaptive Rounding for Post-Training Quantization
When quantizing neural networks, assigning each floating-point weight to...
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06/06/2019
Class-Conditional Compression and Disentanglement: Bridging the Gap between Neural Networks and Naive Bayes Classifiers
In this draft, which reports on work in progress, we 1) adapt the inform...
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04/18/2018
Understanding Individual Neuron Importance Using Information Theory
In this work, we characterize the outputs of individual neurons in a tra...
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03/12/2018
Clustering with Simultaneous Local and Global View of Data: A message passing based approach
A good clustering algorithm should not only be able to discover clusters...
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02/27/2018
How (Not) To Train Your Neural Network Using the Information Bottleneck Principle
In this theory paper, we investigate training deep neural networks (DNNs...
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02/16/2018
A Mismatched Joint Source-Channel Coding Perspective of Probabilistic Amplitude Shaping: Achievable Rates and Error Exponents
Probabilistic Amplitude Shaping (PAS) is a novel method of reliable comm...
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01/02/2018