5 citations · 8 across the 7 of their papers we have counts for
7 papers
A Traditional Approach to Symbolic Piano Continuation
Christian Zhou-Zheng, John Backsund, Dun Li Chan +6
We present a traditional approach to symbolic piano music continuation for the MIREX 2025 Symbolic Music Generation challenge. While computational music generation has recently foc…
ConvergeWriter: Data-Driven Bottom-Up Article Construction
Binquan Ji, Jiaqi Wang, Ruiting Li +6
Large Language Models (LLMs) have shown remarkable prowess in text generation, yet producing long-form, factual documents grounded in extensive external knowledge bases remains a s…
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Drew Prinster, Xing Han, Anqi Liu +1
Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continua…
Quadratic Gating Mixture of Experts: Statistical Insights into Self-Attention
Pedram Akbarian, Huy Nguyen, Xing Han +1
Mixture of Experts (MoE) models are well known for effectively scaling model capacity while preserving computational overheads. In this paper, we establish a rigorous relation betw…
On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions
Huy Nguyen, Xing Han, Carl Harris +2
With the growing prominence of the Mixture of Experts (MoE) architecture in developing large-scale foundation models, we investigate the Hierarchical Mixture of Experts (HMoE), a s…
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
Yuxin Zhang, Lirui Zhao, Mingbao Lin +6
The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towar…
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou +3
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts perf…