A3D3 Highlights AI for Scientific Discovery at NeurIPS

Mingfei Chen (left) and Dr. Eli Shlizerman (right) from the University of Washington presented “AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud Splatting.” They propose a novel approach, AV-Cloud, for rendering high-quality spatial audio in 3D scenes that is in synchrony with the visual stream but does not rely on nor is explicitly conditioned on the visual rendering. [paper link, project link]

Dr. Eli Chien from the Georgia Institute of Technology presented a spotlight poster titled “Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning.” They propose Langevin unlearning which not only connects DP with unlearning, but also provides unlearning privacy guarantees for PNGD with Langevin dynamic analysis. [paper link]

Eli Chien also presented a poster titled “Certified Machine Unlearning via Noisy Stochastic Gradient Descent.” They propose to leverage stochastic noisy gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity assumption. [paper link]

Jingru (Jessica) Jia from the University of Illinois at Urbana-Champaign presented “Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context.” This paper quantitatively assesses the decision-making behaviors of large language models (LLMs), demonstrating how these models mirror human risk behaviors and exhibit significant variations when embedded with socio-demographic characteristics. [paper link]

Shikun Liu from the Georgia Institute of Technology presented “GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts.” They propose GeSS, a comprehensive benchmark designed for evaluating the performance of geometric deep learning (GDL) models in scientific scenarios with various distribution shifts, spanning scientific domains from particle physics and materials science to biochemistry. [paper link]

Yingbing Huang from the University of Illinois Urbana-Champaign presented “SnapKV: LLM Knows What You are Looking for Before Generation.” They propose SnapKV, a fine-tuning-free method to reduce the Key-Value (KV) cache size in Large Language Models (LLMs) by clustering important positions for each attention head. [paper link]

Xiulong Liu from the University of Washington presented “Tell What You Hear From What You See – Video to Audio Generation Through Text.” It is a novel multi-modal generation framework for text guided video-to-audio generation and video-to-audio captioning. [paper link]

Zihan Zhao from the University of California, San Diego presented “Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture” at ML4PS workshop. They propose an approach to learning augmentation-independent jet representations using a jet-based joint embedding predictive architecture (J-JEPA). [paper link, project link]