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…
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…
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…
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…
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…