12 citations · 15 across the 14 of their papers we have counts for
7 papers · 1 filter
Multiscale Gaussian-Mixture Modeling for HMM Post-Processing in Selective Auditory Attention Decoding
Tianyi Li, Simon Geirnaert, Bert De Smedt +1
Selective auditory attention decoding (sAAD) infers from electroencephalography (EEG) which speaker a listener attends to in multi-speaker scenarios. A widely used approach reconst…
DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees
Tianyi Li, Yaxin Luo, Xinyi Shang +1
Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency…
Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
Tianyi Li, Zhiqiang Shen
Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the inte…
Dataset-Level Metrics Attenuate Non-Determinism: A Fine-Grained Non-Determinism Evaluation in Diffusion Language Models
Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen +5
Diffusion language models (DLMs) have emerged as a promising paradigm for large language models (LLMs), yet the non-deterministic behavior of DLMs remains poorly understood. The ex…
Tracing Prompt-Level Trajectories to Understand Student Learning with AI in Programming Education
Tianyu Shao, Miguel Feijóo-García, Yi Zhang +4
As AI tools such as ChatGPT enter programming classrooms, students encounter differing rules across courses and instructors, which shape how they use AI and leave them with unequal…
Sink-Aware Pruning for Diffusion Language Models
Aidar Myrzakhan, Tianyi Li, Bowei Guo +2
Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressi…
MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models
Tianyi Li, Shihui Zang, Moritz Münchmeyer
We develop a general framework to discover scientific algorithms and apply it to three problems in computational cosmology. Our code, MadEvolve, is similar to Google's AlphaEvolve,…