activity
20192026
most citedR-Drop: Regularized Dropout for Neural Networks

306 citations · 700 across the 24 of their papers we have counts for

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12 papers · 1 filter

cs.CL2026

BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language

Qizhi Pei, Zhimeng Zhou, Yi Duan +9

We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules and proteins within a single dec…

cs.CL2024★ 5 cited

Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey

Qizhi Pei, Zhimeng Zhou, Kaiyuan Gao +6

The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and…

cs.CL2023★ 6 cited

BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

Qizhi Pei, Wei Zhang, Jinhua Zhu +5

Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several…

cs.CL2023

AMOM: Adaptive Masking over Masking for Conditional Masked Language Model

Yisheng Xiao, Ruiyang Xu, Lijun Wu +4

Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization,…

cs.CL2022

Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change

Zhaochen Su, Zecheng Tang, Xinyan Guan +3

Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., the language model pre-trained on static data from past…

cs.CL2022★ 2 cited

A Mutually Reinforced Framework for Pretrained Sentence Embeddings

Junhan Yang, Zheng Liu, Shitao Xiao +5

The lack of labeled data is a major obstacle to learning high-quality sentence embeddings. Recently, self-supervised contrastive learning (SCL) is regarded as a promising way to ad…