5 citations · 10 across the 6 of their papers we have counts for
7 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…
FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models
Max Zhu, Adrián Bazaga, Pietro Liò
Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) hav…
Incorporating LLM Priors into Tabular Learners
Max Zhu, Siniša Stanivuk, Andrija Petrovic +2
We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity…
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic +4
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain. Existing few-shot methods are not directly applicable to tabular datasets d…
Modular Neural Ordinary Differential Equations
Max Zhu, Pietro Lio, Jacob Moss
The laws of physics have been written in the language of dif-ferential equations for centuries. Neural Ordinary Differen-tial Equations (NODEs) are a new machine learning architect…