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01/26/2023
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Lossy image compression aims to represent images in as few bits as possi...
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12/28/2022
Latent Discretization for Continuous-time Sequence Compression
Neural compression offers a domain-agnostic approach to creating codecs ...
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12/14/2022
Image Compression with Product Quantized Masked Image Modeling
Recent neural compression methods have been based on the popular hyperpr...
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11/02/2022
An optimal control perspective on diffusion-based generative modeling
We establish a connection between stochastic optimal control and generat...
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07/15/2021
Compressing Multisets with Large Alphabets
Current methods that optimally compress multisets are not suitable for h...
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06/21/2021
Lossy Compression for Lossless Prediction
Most data is automatically collected and only ever "seen" by algorithms....
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02/22/2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Latent variable models have been successfully applied in lossless compre...
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03/30/2020
Neural Communication Systems with Bandwidth-limited Channel
Reliably transmitting messages despite information loss due to a noisy c...
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06/18/2019
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Scientific imaging techniques such as optical and electron microscopy an...
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11/17/2017
Improved Bayesian Compression
Compression of Neural Networks (NN) has become a highly studied topic in...
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07/16/2017
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Optical Music Recognition (OMR) is an important technology within Music ...
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05/24/2017
Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a ...
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02/13/2017