8 papers
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Yixian Xu, Shengjie Luo, Liwei Wang +2
Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting qual…
In-Place Test-Time Training
Guhao Feng, Shengjie Luo, Kai Hua +4
The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new informat…
UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
Jigang Fan, Quanlin Wu, Shengjie Luo +1
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and e…
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
Guhao Feng, Kai Yang, Yuntian Gu +6
Despite the remarkable success of Transformer-based large language models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a signi…
Let the Code LLM Edit Itself When You Edit the Code
Zhenyu He, Jun Zhang, Shengjie Luo +3
In this work, we investigate a typical scenario in code generation where a developer edits existing code in real time and requests a code assistant, e.g., a large language model, t…
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves…