7 papers
UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching
Qilin Huang, Quynh Anh Huynh, Long Le +5
Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the r…
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
Ryan Lucas, Mehdi Makni, Xiang Meng +2
Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for co…
Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction
Ryan Lucas, Kayhan Behdin, Zhipeng Wang +3
Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale. We show that using compression t…
A GPU-accelerated Nonlinear Branch-and-Bound Framework for Sparse Linear Models
Xiang Meng, Ryan Lucas, Rahul Mazumder
We study exact sparse linear regression with an penalty and develop a branch-and-bound (BnB) algorithm explicitly designed for GPU execution. Starting from a perspe…
Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels
Long Le, Ryan Lucas, Chen Wang +4
Inferring the physical properties of 3D scenes from visual information is a critical yet challenging task for creating interactive and realistic virtual worlds. While humans intuit…
Holistic Robust Data-Driven Decisions
Amine Bennouna, Bart Van Parys, Ryan Lucas
The design of data-driven formulations for machine learning and decision-making with good out-of-sample performance is a key challenge. The observation that good in-sample performa…