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Richard Jiang

3 accepted papers

2026

Neural Differentiation in Deep Networks: A Theoretical Framework for Expressivity and Representational Diversity

CVPR 2026

We begin by developing a mathematical framework of neural differentiation, formulated at the level of individual neurons. This framework formalizes the principle that each neuron should acquire a distinct representational role within the network, thereby avoiding redundancy and maximizing collective

Cited by 0SourceScholar
2025

Energy Landscape-Aware Vision Transformers: Layerwise Dynamics and Adaptive Task-Specific Training via Hopfield States

NeurIPS 2025poster

Recent advances in Vision Transformers (ViTs) have shown remarkable performance across vision tasks, yet their deep, uniform layer structure introduces significant computational overhead. In this work, we explore the emergent dynamics of ViT layers through the lens of energy-based memory systems, dr…

Cited by 0SourceScholar