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pro vyhledávání: '"Guo, Hongyu"'
In self-supervised contrastive learning, a widely-adopted objective function is InfoNCE, which uses the heuristic cosine similarity for the representation comparison, and is closely related to maximizing the Kullback-Leibler (KL)-based mutual informa
Externí odkaz:
http://arxiv.org/abs/2402.10150
Autor:
Liu, Shengchao, Du, Weitao, Li, Yanjing, Li, Zhuoxinran, Bhethanabotla, Vignesh, Rampal, Nakul, Yaghi, Omar, Borgs, Christian, Anandkumar, Anima, Guo, Hongyu, Chayes, Jennifer
In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. There has been a long history of improving the e
Externí odkaz:
http://arxiv.org/abs/2401.15122
Autor:
Guo, Hongyu
This paper proposes a self-calibrated transit service monitoring framework that aims to obtain the performance of a transit system using automated collected data. We first introduce an event-based transit simulation model, which allows the detailed s
Externí odkaz:
http://arxiv.org/abs/2401.03121
In this work, we highlight and perform a comprehensive study on calibration attacks, a form of adversarial attacks that aim to trap victim models to be heavily miscalibrated without altering their predicted labels, hence endangering the trustworthine
Externí odkaz:
http://arxiv.org/abs/2401.02718
Autor:
Liu, Shengchao, Du, Weitao, Li, Yanjing, Li, Zhuoxinran, Zheng, Zhiling, Duan, Chenru, Ma, Zhiming, Yaghi, Omar, Anandkumar, Anima, Borgs, Christian, Chayes, Jennifer, Guo, Hongyu, Tang, Jian
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery. For these scientific probl
Externí odkaz:
http://arxiv.org/abs/2306.09375
Autor:
Liu, Shengchao, Wang, Jiongxiao, Yang, Yijin, Wang, Chengpeng, Liu, Ling, Guo, Hongyu, Xiao, Chaowei
Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversa
Externí odkaz:
http://arxiv.org/abs/2305.18090
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods foc
Externí odkaz:
http://arxiv.org/abs/2305.18407
Autor:
Guo, Hongyu
Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. Assessing the production of wheat spikes can help us measure the grain production. Thus, detecting and characterizing spikes from image
Externí odkaz:
http://arxiv.org/abs/2303.10542
Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is comparable or superior to fine-tuning while dramatically reducing trainable
Externí odkaz:
http://arxiv.org/abs/2303.02577
T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as
Externí odkaz:
http://arxiv.org/abs/2303.02162