58 citations · 79 across the 7 of their papers we have counts for
8 papers
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Haozhe Zhao, Zefan Cai, Shuzheng Si +7
Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utiliz…
The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 38th International Cosmic Ray Conference (ICRC 2023)
GRAND Collaboration, Rafael Alves Batista, Aurélien Benoit-Lévy +96
The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses la…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction
Cai Yang, Addie Woicik, Hoifung Poon +1
Language models pre-trained on scientific literature corpora have substantially advanced scientific discovery by offering high-quality feature representations for downstream applic…
Social Welfare Maximization for Collaborative Edge Computing: A Deep Reinforcement Learning-Based Approach
Xingqiu He, Yuhang Shen, Hongxi Zhu +3
Collaborative Edge Computing (CEC) is an effective method that improves the performance of Mobile Edge Computing (MEC) systems by offloading computation tasks from busy edge server…
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
Mehmet Saygın Seyfioğlu, Zixuan Liu, Pranav Kamath +4
We propose a novel framework for Alzheimer's disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), wh…
A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions
Zechen Wang, Liangzhen Zheng, Sheng Wang +6
The machine learning (ML) and deep learning (DL) techniques are widely recognized to be powerful tools for virtual drug screening. The recently reported ML- or DL-based scoring fun…
ProTranslator: zero-shot protein function prediction using textual description
Hanwen Xu, Sheng Wang
Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing co…