Search Results for author: Fei Ye

Found 27 papers, 14 papers with code

Diffusion Language Models Are Versatile Protein Learners

no code implementations28 Feb 2024 Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, ShuJian Huang, Quanquan Gu

This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences.

Protein Language Model

Learning Harmonic Molecular Representations on Riemannian Manifold

1 code implementation27 Mar 2023 Yiqun Wang, Yuning Shen, Shi Chen, Lihao Wang, Fei Ye, Hao Zhou

In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.

Drug Discovery molecular representation +2

Structure-informed Language Models Are Protein Designers

1 code implementation3 Feb 2023 Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu

This paper demonstrates that language models are strong structure-based protein designers.

On Pre-trained Language Models for Antibody

1 code implementation28 Jan 2023 Danqing Wang, Fei Ye, Hao Zhou

The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks.

Drug Discovery Language Modelling +1

Self-Evolved Dynamic Expansion Model for Task-Free Continual Learning

1 code implementation ICCV 2023 Fei Ye, Adrian G. Bors

In this paper, we propose a novel and effective framework for TFCL, which dynamically expands the architecture of a DEM model through a self-assessment mechanism evaluating the diversity of knowledge among existing experts as expansion signals.

Continual Learning Transfer Learning

Wasserstein Expansible Variational Autoencoder for Discriminative and Generative Continual Learning

1 code implementation ICCV 2023 Fei Ye, Adrian G. Bors

Despite promising achievements by the Variational Autoencoder (VAE) mixtures in continual learning, such methods ignore the redundancy among the probabilistic representations of their components when performing model expansion, leading to mixture components learning similar tasks.

Continual Learning

Accelerating Antimicrobial Peptide Discovery with Latent Structure

1 code implementation28 Nov 2022 Danqing Wang, Zeyu Wen, Fei Ye, Lei LI, Hao Zhou

By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures.

Quantization

Task-Free Continual Learning via Online Discrepancy Distance Learning

no code implementations12 Oct 2022 Fei Ye, Adrian G. Bors

This paper develops a new theoretical analysis framework which provides generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model.

Continual Learning Generalization Bounds

Continual Variational Autoencoder Learning via Online Cooperative Memorization

1 code implementation20 Jul 2022 Fei Ye, Adrian G. Bors

Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks.

Continual Learning Memorization

Learning an evolved mixture model for task-free continual learning

no code implementations11 Jul 2022 Fei Ye, Adrian G. Bors

In this paper, we address a more challenging and realistic setting in CL, namely the Task-Free Continual Learning (TFCL) in which a model is trained on non-stationary data streams with no explicit task information.

Continual Learning

Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Model

1 code implementation25 Mar 2022 Fei Ye, Adrian G. Bors

In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model.

Lifelong Generative Modelling Using Dynamic Expansion Graph Model

1 code implementation15 Dec 2021 Fei Ye, Adrian G. Bors

In this paper we study the forgetting behaviour of VAEs using a joint GR and ENA methodology, by deriving an upper bound on the negative marginal log-likelihood.

Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process

1 code implementation ICCV 2021 Fei Ye, Adrian G. Bors

Recent research efforts in lifelong learning propose to grow a mixture of models to adapt to an increasing number of tasks.

Lifelong Mixture of Variational Autoencoders

1 code implementation9 Jul 2021 Fei Ye, Adrian G. Bors

The mixing coefficients in the mixture, control the contributions of each expert in the goal representation.

Lifelong Twin Generative Adversarial Networks

no code implementations9 Jul 2021 Fei Ye, Adrian G. Bors

In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs).

Knowledge Distillation

InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood

no code implementations9 Jul 2021 Fei Ye, Adrian G. Bors

Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold.

Representation Learning

Lifelong Teacher-Student Network Learning

1 code implementation9 Jul 2021 Fei Ye, Adrian G. Bors

While the Student module is trained with a new given database, the Teacher module would remind the Student about the information learnt in the past.

Generative Adversarial Network

A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

no code implementations29 May 2021 Fei Ye, Shen Zhang, Pin Wang, Ching-Yao Chan

In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles.

Autonomous Driving Motion Planning +1

ESAD: End-to-end Deep Semi-supervised Anomaly Detection

no code implementations9 Dec 2020 Chaoqin Huang, Fei Ye, Peisen Zhao, Ya zhang, Yan-Feng Wang, Qi Tian

This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided.

Ranked #25 on Anomaly Detection on One-class CIFAR-10 (using extra training data)

Decoder Medical Diagnosis +2

Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework

no code implementations9 Dec 2020 Fei Ye, Huangjie Zheng, Chaoqin Huang, Ya zhang

Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection.

Anomaly Detection

Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles

no code implementations28 Aug 2020 Fei Ye, Pin Wang, Ching-Yao Chan, Jiucai Zhang

The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density.

Autonomous Vehicles Imitation Learning +3

Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning

no code implementations25 Jul 2020 Shen Zhang, Fei Ye, Bingnan Wang, Thomas G. Habetler

Most of the data-driven approaches applied to bearing fault diagnosis up-to-date are trained using a large amount of fault data collected a priori.

Anomaly Detection Few-Shot Learning

Learning latent representations across multiple data domains using Lifelong VAEGAN

1 code implementation ECCV 2020 Fei Ye, Adrian G. Bors

The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.

Representation Learning

Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

no code implementations7 Feb 2020 Fei Ye, Xuxin Cheng, Pin Wang, Ching-Yao Chan, Jiucai Zhang

The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.

Autonomous Driving reinforcement-learning +1

Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

no code implementations2 Dec 2019 Shen Zhang, Fei Ye, Bingnan Wang, Thomas G. Habetler

Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori.

Anomaly Detection

Attribute Restoration Framework for Anomaly Detection

1 code implementation25 Nov 2019 Chaoqin Huang, Fei Ye, Jinkun Cao, Maosen Li, Ya zhang, Cewu Lu

We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.

Anomaly Detection Attribute +1

Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems

no code implementations29 Apr 2018 Haiyang Wang, Yong Tang, Ziyang Jia, Fei Ye

Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration.

Ensemble Learning General Classification

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