Minimal Random Code Learning: Getting Bits Back from Compressed Model
Parameters

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters

30 September 2018

José Miguel Hernández-Lobato

Papers citing "Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters"

9 / 9 papers shown

Title
Lossy Compression with Pretrained Diffusion Models Jeremy Vonderfecht Feng Liu DiffM 130 2 0 20 Jan 2025
On the Challenges and Opportunities in Generative AI Laura Manduchi Kushagra Pandey Robert Bamler Ryan Cotterell Sina Daubener ... F. Wenzel Frank Wood Stephan Mandt Vincent Fortuin Vincent Fortuin 187 21 0 28 Feb 2024
Weightless: Lossy Weight Encoding For Deep Neural Network Compression Brandon Reagen Udit Gupta Bob Adolf Michael Mitzenmacher Alexander M. Rush Gu-Yeon Wei David Brooks 52 38 0 13 Nov 2017
Bayesian Compression for Deep Learning Christos Louizos Karen Ullrich Max Welling UQCV BDL 147 479 0 24 May 2017
The sample size required in importance sampling S. Chatterjee P. Diaconis 86 191 0 04 Nov 2015
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 237 8,821 0 01 Oct 2015
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 292 6,660 0 08 Jun 2015
Compressing Neural Networks with the Hashing Trick Wenlin Chen James T. Wilson Stephen Tyree Kilian Q. Weinberger Yixin Chen 145 1,191 0 19 Apr 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.5K 149,842 0 22 Dec 2014