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Found 6 papers, 3 papers with code
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
An important yet underexplored question in the PAC-Bayes literature is how much tightness we lose by restricting the posterior family to factorized Gaussian distributions when optimizing a PAC-Bayes bound.
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quantization and enables direct optimization of the rate-distortion performance.
Minimal Random Code Learning with Mean-KL Parameterization
To achieve the desired compression rate, $D_{\mathrm{KL}}[Q_{\mathbf{w}} \Vert P_{\mathbf{w}}]$ must be constrained, which requires a computationally expensive annealing procedure under the conventional mean-variance (Mean-Var) parameterization for $Q_{\mathbf{w}}$.
Compression with Bayesian Implicit Neural Representations
Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image.
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Variational Autoencoders (VAEs) have seen widespread use in learned image compression.
Compression without Quantization
Standard compression algorithms work by mapping an image to discrete code using an encoder from which the original image can be reconstructed through a decoder.
