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of 36
pro vyhledávání: '"Zhang, Enzhi"'
Autor:
Chen, Yuxin, Tan, Junfei, Zhang, An, Yang, Zhengyi, Sheng, Leheng, Zhang, Enzhi, Wang, Xiang, Chua, Tat-Seng
Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities. M
Externí odkaz:
http://arxiv.org/abs/2406.09215
This paper introduces a novel competitive mechanism into differential evolution (DE), presenting an effective DE variant named competitive DE (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed
Externí odkaz:
http://arxiv.org/abs/2406.05436
Autor:
Liu, Zhiyuan, Shi, Yaorui, Zhang, An, Li, Sihang, Zhang, Enzhi, Wang, Xiang, Kawaguchi, Kenji, Chua, Tat-Seng
Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthes
Externí odkaz:
http://arxiv.org/abs/2405.14225
Autor:
Liu, Zhiyuan, Zhang, An, Fei, Hao, Zhang, Enzhi, Wang, Xiang, Kawaguchi, Kenji, Chua, Tat-Seng
Language Models (LMs) excel in understanding textual descriptions of proteins, as evident in biomedical question-answering tasks. However, their capability falters with raw protein data, such as amino acid sequences, due to a deficit in pretraining o
Externí odkaz:
http://arxiv.org/abs/2405.12564
Autor:
Zhang, Enzhi, Lyngaas, Isaac, Chen, Peng, Wang, Xiao, Igarashi, Jun, Huo, Yuankai, Wahib, Mohamed, Munetomo, Masaharu
Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear se
Externí odkaz:
http://arxiv.org/abs/2404.09707
Autor:
Liu, Zhiyuan, Shi, Yaorui, Zhang, An, Zhang, Enzhi, Kawaguchi, Kenji, Wang, Xiang, Chua, Tat-Seng
Masked graph modeling excels in the self-supervised representation learning of molecular graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three key components: (1) graph tokenizer, which breaks a molecular graph into
Externí odkaz:
http://arxiv.org/abs/2310.14753
Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably those employing Graph Neural Networks (GNNs), are often limited by insuff
Externí odkaz:
http://arxiv.org/abs/2310.13590
In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitnes
Externí odkaz:
http://arxiv.org/abs/2210.06814
In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can accelerate the c
Externí odkaz:
http://arxiv.org/abs/2209.13077
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