Search Results for author: Zhangyang Gao

Found 32 papers, 20 papers with code

Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training

1 code implementation23 Nov 2023 Cheng Tan, Jingxuan Wei, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Xihong Yang, Stan Z. Li

Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning.

General Point Model with Autoencoding and Autoregressive

no code implementations25 Oct 2023 Zhe Li, Zhangyang Gao, Cheng Tan, Stan Z. Li, Laurence T. Yang

This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks.

Language Modelling Large Language Model +2

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

no code implementations14 Oct 2023 Yufei Huang, Siyuan Li, Jin Su, Lirong Wu, Odin Zhang, Haitao Lin, Jingqi Qi, Zihan Liu, Zhangyang Gao, Yuyang Liu, Jiangbin Zheng, Stan. ZQ. Li

To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.

Graph structure learning Property Prediction +2

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View

no code implementations9 Oct 2023 Cheng Tan, Jue Wang, Zhangyang Gao, Siyuan Li, Lirong Wu, Jun Xia, Stan Z. Li

In this paper, we re-examine the two dominant temporal modeling approaches within the realm of spatio-temporal predictive learning, offering a unified perspective.

Self-Supervised Learning

Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework

1 code implementation24 Jul 2023 Jingxuan Wei, Cheng Tan, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Bihui Yu, Ruifeng Guo, Stan Z. Li

Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks.

Contrastive Learning Multiple-choice

Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement

1 code implementation20 May 2023 Zhangyang Gao, Cheng Tan, Stan Z. Li

After witnessing the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further.

Protein Design Retrieval +1

Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer

no code implementations21 Apr 2023 Cheng Tan, Zhangyang Gao, Stan Z. Li

In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.


PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

no code implementations14 Feb 2023 Zhangyang Gao, Yuqi Hu, Cheng Tan, Stan Z. Li

Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties?

Multi-Task Learning

Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

1 code implementation25 Jan 2023 Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao, Stan Z. Li

We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.

Contrastive Learning Protein Design +2

DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints

1 code implementation22 Jan 2023 Zhangyang Gao, Cheng Tan, Stan Z. Li

Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling?

Denoising Language Modelling

RFold: RNA Secondary Structure Prediction with Decoupled Optimization

1 code implementation2 Dec 2022 Cheng Tan, Zhangyang Gao, Stan Z. Li

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction.

SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning

2 code implementations22 Nov 2022 Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li

Without introducing any extra tricks and strategies, SimVP can achieve superior performance on various benchmark datasets.

Video Prediction

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

no code implementations5 Oct 2022 Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Zicheng Liu, Guojiang Zhao, Stan Z. Li

Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.

Classification Node Classification

PiFold: Toward effective and efficient protein inverse folding

1 code implementation22 Sep 2022 Zhangyang Gao, Cheng Tan, Pablo Chacón, Stan Z. Li

How can we design protein sequences folding into the desired structures effectively and efficiently?

Protein Design

A Survey on Generative Diffusion Model

1 code implementation6 Sep 2022 Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li

Deep generative models are a prominent approach for data generation, and have been used to produce high quality samples in various domains.

Dimensionality Reduction

CoSP: Co-supervised pretraining of pocket and ligand

no code implementations23 Jun 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space?

Contrastive Learning Specificity

SimVP: Simpler yet Better Video Prediction

3 code implementations CVPR 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies.

Video Prediction

Hyperspherical Consistency Regularization

1 code implementation CVPR 2022 Cheng Tan, Zhangyang Gao, Lirong Wu, Siyuan Li, Stan Z. Li

Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier.

Contrastive Learning Self-Supervised Learning +1

Generative De Novo Protein Design with Global Context

1 code implementation21 Apr 2022 Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li

Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.

Protein Design Protein Structure Prediction

SemiRetro: Semi-template framework boosts deep retrosynthesis prediction

no code implementations12 Feb 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods.

Graph Learning Retrosynthesis

Target-aware Molecular Graph Generation

no code implementations10 Feb 2022 Cheng Tan, Zhangyang Gao, Stan Z. Li

Building on the recent advantages of flow-based molecular generation models, we propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space.

Drug Discovery Graph Generation +1

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB

1 code implementation1 Feb 2022 Zhangyang Gao, Cheng Tan, Stan Z. Li

While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges.

Protein Design Protein Folding

An Empirical Study: Extensive Deep Temporal Point Process

1 code implementation19 Oct 2021 Haitao Lin, Cheng Tan, Lirong Wu, Zhangyang Gao, Stan. Z. Li

In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization.

Graph structure learning Variational Inference

Git: Clustering Based on Graph of Intensity Topology

2 code implementations4 Oct 2021 Zhangyang Gao, Haitao Lin, Cheng Tan, Lirong Wu, Stan. Z Li

\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm.

Clustering Clustering Algorithms Evaluation

Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive

1 code implementation16 May 2021 Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan. Z. Li

In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data.

Self-Supervised Learning

Conditional Local Convolution for Spatio-temporal Meteorological Forecasting

1 code implementation4 Jan 2021 Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, Stan. Z. Li

We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.

Spatio-Temporal Forecasting Weather Forecasting

Towards Robust Graph Neural Networks against Label Noise

no code implementations1 Jan 2021 Jun Xia, Haitao Lin, Yongjie Xu, Lirong Wu, Zhangyang Gao, Siyuan Li, Stan Z. Li

A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.

Learning with noisy labels Meta-Learning +2

LookHops: light multi-order convolution and pooling for graph classification

no code implementations28 Dec 2020 Zhangyang Gao, Haitao Lin, Stan. Z Li

Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications.

General Classification Graph Classification

Clustering Based on Graph of Density Topology

1 code implementation24 Sep 2020 Zhangyang Gao, Haitao Lin, Stan Z. Li

GDT jointly considers the local and global structures of data samples: firstly forming local clusters based on a density growing process with a strategy for properly noise handling as well as cluster boundary detection; and then estimating a GDT from relationship between local clusters in terms of a connectivity measure, givingglobal topological graph.

Boundary Detection Clustering

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