5 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks
Max Zhu, Bohang Sun, Pietro Lio +1
Reducing memory use in large language model (LLM) training is an ongoing challenge. Activation checkpointing saves memory by recomputation, while lossy compression changes the stor…
LCT: Losslessly Compressed Tensors with GPU friendly execution for Training
Max Zhu, Bohang Sun, Pietro Lio +1
Lossless tensor compression could reduce memory usage during training, but training requires tensors to be repeatedly re-encoded. We introduce Losslessly Compressed Tensors (LCT),…
A Causal Foundation Model for Structure and Outcome Prediction
Max Zhu, Martino Mansoldo, Ching-Hao Wang +1
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, support…
The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
Bohang Sun, Max Zhu, Francesco Caso +5
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tok…