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Chun Zhang

8 accepted papers

2026

SD-MoE: Spectral Decomposition for Effective Expert Specialization

ICML 2026poster

Mixture-of-Experts (MoE) architectures scale Large Language Models via expert specialization induced by conditional computation. In practice, however, expert specialization often fails: some experts become functionally similar, while others functioning as de facto shared experts, limiting the effect…

Cited by 0SourceScholar
2026

Unifying Channel Independence and Mixing: Multi-Scale Patch Recursion for Global–Local Representation Synergy in Multivariate Time Series Forecasting

AAAI 2026technical

Multivariate time series forecasting underpins applications in finance, meteorology, and industrial operations. Yet two persistent hurdles remain: (i) models typically choose between Channel–Independent (CI) and Channel–Mixed (CM) formulations—each with distinct strengths—leading to large performanc

Cited by 0SourcePDFScholar
2025

Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference

ICML 2025poster

Mixture-of-Experts (MoE) is widely adopted to deploy Large Language Models (LLMs) on edge devices with limited memory budgets. Although MoE is, in theory, an inborn memory-friendly architecture requiring only a few activated experts to reside in the memory for inference, current MoE architectures ca…

Cited by 0SourcePDFScholar
2025

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

NeurIPS 2025poster

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs…

Cited by 215SourceScholar
2024

SLR: Learning Quadruped Locomotion without Privileged Information

CoRL 2024poster

Traditional reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieve…

Cited by 4SourceScholar
2018

A Dual-Modal Vision-Based Tactile Sensor for Robotic Hand Grasping

ICRA 2018poster

Humans' fingertips can perceive not only the magnitude and the direction of force but also the texture of object. When we grasp an object, the surface texture sensing of the fingertip helps us recognize the object and the force feeling that is parallel to the skin helps us grasp stably. Focusing on…

Cited by 64SourceScholar