Search Results for author:
Found 27 papers, 14 papers with code
Diffusion Language Models Are Versatile Protein Learners
This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences.
Learning Harmonic Molecular Representations on Riemannian Manifold
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.
Structure-informed Language Models Are Protein Designers
This paper demonstrates that language models are strong structure-based protein designers.
On Pre-trained Language Models for Antibody
The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks.
Wasserstein Expansible Variational Autoencoder for Discriminative and Generative Continual Learning
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.
Self-Evolved Dynamic Expansion Model for Task-Free Continual Learning
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.
Accelerating Antimicrobial Peptide Discovery with Latent Structure
By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures.
Task-Free Continual Learning via Online Discrepancy Distance Learning
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 Variational Autoencoder Learning via Online Cooperative Memorization
Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks.
Learning an evolved mixture model for task-free continual learning
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.
Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Model
In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model.
Lifelong Generative Modelling Using Dynamic Expansion Graph Model
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
Recent research efforts in lifelong learning propose to grow a mixture of models to adapt to an increasing number of tasks.
Lifelong Teacher-Student Network Learning
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.
Lifelong Twin Generative Adversarial Networks
In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs).
InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold.
Lifelong Mixture of Variational Autoencoders
The mixing coefficients in the mixture, control the contributions of each expert in the goal representation.
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
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.
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection.
Ranked #8 on
Anomaly Detection
on One-class CIFAR-100
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
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)
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
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.
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning
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.
Learning latent representations across multiple data domains using Lifelong VAEGAN
The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders
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.
Attribute Restoration Framework for Anomaly Detection
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.
Ranked #21 on
Anomaly Detection
on One-class CIFAR-10
Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems
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.
