6 papers · 1 filter
Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing
Srinivas Anumasa, Rushi Shah, Qiran Zou +1
Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging. Deep ensembles…
FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
Qiran Zou, Hou Hei Lam, Wenhao Zhao +11
AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing…
Mitigating Premature Discretization with Progressive Quantization for Robust Vector Tokenization
Wenhao Zhao, Qiran Zou, Zhouhan Lin +1
Vector Quantization (VQ) has become the cornerstone of tokenization for many multimodal Large Language Models and diffusion synthesis. However, existing VQ paradigms suffer from a…
Early Quantization Shrinks Codebook: A Simple Fix for Diversity-Preserving Tokenization
Wenhao Zhao, Qiran Zou, Rushi Shah +3
Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data represen…
Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discovery
Tingting Chen, Beibei Lin, Srinivas Anumasa +5
Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in noisy, open-ended scientific environments,…
Representation Collapsing Problems in Vector Quantization
Wenhao Zhao, Qiran Zou, Rushi Shah +1
Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data represen…