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Found 15 papers, 8 papers with code
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Using SMolInstruct, we fine-tune a set of open-source LLMs, among which, we find that Mistral serves as the best base model for chemistry tasks.
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
As widely demonstrated in the literature, this issue could lead to a loss of information in individual items, and significantly degrade models' scalability and performance.
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product.
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.
Nearest-Neighbor Sampling Based Conditional Independence Testing
The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data.
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy
This process is known as TCR recognition and constitutes a key step for immune response.
$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants.
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://github. com/yd1008/ts_ensemble_sunspot.
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks
Eddy current testing (ECT) is an effective technique in the evaluation of the depth of metal surface defects.
A Deep Generative Model for Molecule Optimization via One Fragment Modification
A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response.
Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs.
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images.
