6 citations · 6 across the 6 of their papers we have counts for
7 papers
ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert Routing
Mingrui Li, Sixian Shen, Minzhang Li +4
Proteins perform diverse cellular functions, and even single amino-acid substitutions can alter stability, activity, or molecular interactions. Protein language models (PLMs) provi…
Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs
Zhangzhi Xiong, Minzhang Li, Haotian Yu +6
Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budg…
Parallel Complex Diffusion for Scalable Time Series Generation
Rongyao Cai, Yuxi Wan, Kexin Zhang +4
Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time seri…
From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation
Rongyao Cai, Ming Jin, Qingsong Wen +1
Domain shift poses a fundamental challenge in time series analysis, where models trained on source domain often fail dramatically when applied in target domain with different yet s…
Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience
Andrew Chang, Chenkai Hu, Ji Qi +5
Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment…
Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
Holden Lee, Kexin Zhang
We propose using a modified conductance-based method to study the mixing time of an important class of two-block Gibbs samplers, the data augmentation (DA) algorithm. %, which is o…
Position: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yifan Zhang, Wei Chen +6
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently mad…