Search Results for author: Yuedong Yang

Found 15 papers, 9 papers with code

Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction

1 code implementation16 Oct 2023 Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen

Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships.

Contrastive Learning Knowledge Graph Embedding +1

EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency

1 code implementation1 Aug 2023 Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen

In this paper, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation.


TIPS: Topologically Important Path Sampling for Anytime Neural Networks

no code implementations13 May 2023 Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu

Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints.

Retrieval-based Knowledge Augmented Vision Language Pre-training

no code implementations27 Apr 2023 Jiahua Rao, Zifei Shan, Longpo Liu, Yao Zhou, Yuedong Yang

With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.

Entity Linking Knowledge Graphs +5

ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients

1 code implementation26 Jan 2023 Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, Radu Marculescu

Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params.

Image Classification Neural Architecture Search

Efficient On-device Training via Gradient Filtering

1 code implementation CVPR 2023 Yuedong Yang, Guihong Li, Radu Marculescu

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI.

Federated Learning Image Classification +1

Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction

1 code implementation12 May 2022 Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang

To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.

Gene Interaction Prediction Representation Learning

SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning

no code implementations31 Jan 2022 Zihui Xue, Yuedong Yang, Mengtian Yang, Radu Marculescu

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition.

Drug Discovery Edge-computing +3

Molecular Attributes Transfer from Non-Parallel Data

no code implementations30 Nov 2021 Shuangjia Zheng, Ying Song, Zhang Pan, Chengtao Li, Le Song, Yuedong Yang

Optimizing chemical molecules for desired properties lies at the core of drug development.

Style Transfer

Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning

no code implementations26 Jul 2021 Shuangjia Zheng, Sijie Mai, Ya Sun, Haifeng Hu, Yuedong Yang

In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings.

Inductive Link Prediction Knowledge Graphs +1

Learning Attributed Graph Representations with Communicative Message Passing Transformer

1 code implementation19 Jul 2021 Jianwen Chen, Shuangjia Zheng, Ying Song, Jiahua Rao, Yuedong Yang

For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture.

Inductive Bias molecular representation +1

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

2 code implementations1 Jul 2021 Jiahua Rao, Shuangjia Zheng, Yuedong Yang

Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.

Drug Discovery Explainable artificial intelligence +3

BioNavi-NP: Biosynthesis Navigator for Natural Products

no code implementations26 May 2021 Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu

Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.


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