Search Results for author: Minghao Xu

Found 22 papers, 17 papers with code

A Systematic Study of Joint Representation Learning on Protein Sequences and Structures

3 code implementations11 Mar 2023 Zuobai Zhang, Chuanrui Wang, Minghao Xu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang

Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge.

Contrastive Learning Protein Function Prediction +1

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

1 code implementation5 Jun 2022 Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang

However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.

Feature Engineering Multi-Task Learning +2

HIRL: A General Framework for Hierarchical Image Representation Learning

1 code implementation26 May 2022 Minghao Xu, Yuanfan Guo, Xuanyu Zhu, Jiawen Li, Zhenbang Sun, Jian Tang, Yi Xu, Bingbing Ni

This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained.

Image Clustering Representation Learning +3

Protein Representation Learning by Geometric Structure Pretraining

2 code implementations11 Mar 2022 Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang

Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function.

Contrastive Learning Property Prediction +1

TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

1 code implementation16 Feb 2022 Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang

However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.

BIG-bench Machine Learning Drug Discovery +2

HCSC: Hierarchical Contrastive Selective Coding

2 code implementations CVPR 2022 Yuanfan Guo, Minghao Xu, Jiawen Li, Bingbing Ni, Xuanyu Zhu, Zhenbang Sun, Yi Xu

In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space.

Contrastive Learning Representation Learning

Joint Modeling of Visual Objects and Relations for Scene Graph Generation

no code implementations NeurIPS 2021 Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang

We further propose an efficient and effective algorithm for inference based on mean-field variational inference, in which we first provide a warm initialization by independently predicting the objects and their relations according to the current model, followed by a few iterations of relational reasoning.

Graph Generation Knowledge Graph Embedding +5

PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation

1 code implementation ICCV 2021 Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma

Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.

Domain Adaptation object-detection +4

Self-supervised Graph-level Representation Learning with Local and Global Structure

1 code implementation8 Jun 2021 Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang

This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery.

Graph Representation Learning

Graphical Modeling for Multi-Source Domain Adaptation

2 code implementations27 Apr 2021 Minghao Xu, Hang Wang, Bingbing Ni

Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation.

Domain Adaptation

GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement

no code implementations1 Jan 2021 Minghao Xu, Hang Wang, Bingbing Ni, Wenjun Zhang, Jian Tang

We propose to disentangle graph structure and node attributes into two distinct sets of representations, and such disentanglement can be done in either the input or the embedding space.

Attribute Disentanglement +1

Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

1 code implementation ECCV 2020 Hang Wang, Minghao Xu, Bingbing Ni, Wenjun Zhang

Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation.

Domain Adaptation Multi-Source Unsupervised Domain Adaptation

Cross-domain Detection via Graph-induced Prototype Alignment

1 code implementation CVPR 2020 Minghao Xu, Hang Wang, Bingbing Ni, Qi Tian, Wenjun Zhang

To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations.

Domain Adaptation object-detection +1

Adversarial Domain Adaptation with Domain Mixup

1 code implementation4 Dec 2019 Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang

In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.

Domain Adaptation

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