Search Results for author:
Found 25 papers, 14 papers with code
An Autonomous Large Language Model Agent for Chemical Literature Data Mining
The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry.
Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment
Large language models (LLMs) such as GPT-3, OPT, and LLaMA have demonstrated remarkable accuracy in a wide range of tasks.
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society.
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels.
Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently.
Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform
The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery.
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.
Learning Large-scale Network Embedding from Representative Subgraph
Then, NES computes the network embedding from this representative subgraph, efficiently.
Stable Prediction on Graphs with Agnostic Distribution Shift
The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions
RetroPrime achieves the Top-1 accuracy of 64. 8% and 51. 4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset.
Ranked #27 on
Single-step retrosynthesis
on USPTO-50k
Fast Extraction of Word Embedding from Q-contexts
In this work, we show that with merely a small fraction of contexts (Q-contexts)which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors.
Modeling Protein Using Large-scale Pretrain Language Model
The emergence of deep learning models makes modeling data patterns in large quantities of data possible.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
FastMoE: A Fast Mixture-of-Expert Training System
However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Ranked #4 on
Language Modelling
on WikiText-103
(using extra training data)
Local Clustering Graph Neural Networks
Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
Blockwise Self-Attention for Long Document Understanding
We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies.
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.
DeepInf: Social Influence Prediction with Deep Learning
Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.
Revisiting Knowledge Base Embedding as Tensor Decomposition
We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
