10 papers
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…
Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
Dat H. Do, Rushi Shah, Duc V. Le +1
Compositionality is believed to be the foundation for generalization, enabling models to reuse meaningful primitives in novel combinations. Yet, models trained with standard gradie…
JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram +4
Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel diffusion is effective but com…
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…
Navigating heterogeneous protein landscapes through geometry-aware smoothing
Srinivas Anumasa, Barath Chandran, Tingting Chen +15
The evolutionary fitness landscape of biological molecules is extremely sparse and heterogeneous, with functional sequences forming isolated dense ``islands'' within a vast combina…
CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis
Yiming Tang, Wenjia Zhong, Rushi Shah +1
Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black-box nature of their predictions…
Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm
Kaisen Yang, Tinghe Zhang, Rushi Shah +4
Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or split across separate component…
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…
Gaussian Mixture Vector Quantization with Aggregated Categorical Posterior
Mingyuan Yan, Jiawei Wu, Rushi Shah +1
The vector quantization is a widely used method to map continuous representation to discrete space and has important application in tokenization for generative mode, bottlenecking…
Improving Discrete Optimisation Via Decoupled Straight-Through Estimator
Rushi Shah, Mingyuan Yan, Michael Curtis Mozer +1
The Straight-Through Estimator (STE) is the dominant method for training neural networks with discrete variables, enabling gradient-based optimisation by routing gradients through…