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Works297 from public data
- Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life968
A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications.
- Online learning: A comprehensive survey871
This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques.
- Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks863
A novel bi-directional graph model is proposed, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics of rumors by operating on both top-down and bottom-up propagation of rumors.
- Efficient Test-Time Model Adaptation without Forgetting740
An active sample selection criterion is proposed to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation, and a Fisher regularizer is introduced to constrain important model parameters from drastic changes.
- Towards Stable Test-Time Adaptation in Dynamic Wild World617
This paper proposes a sharpness-aware and reliable entropy minimization method, called SAR, for further stabilizing TTA from two aspects: 1) remove partial noisy samples with large gradients, 2) encourage model weights to go to a flat minimum so that the model is robust to the remaining noisy samples.
- Graph Convolutional Networks for Temporal Action Localization572
This paper builds an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge and applies the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
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- Mastering Complex Control in MOBA Games with Deep Reinforcement Learning432
A deep reinforcement learning framework to tackle the problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games is presented, which is of low coupling and high scalability, which enables efficient explorations at large scale.
- Stochastic Optimization with Importance Sampling for Regularized Loss Minimization393
Stochastic optimization with importance sampling is studied, which improves the convergence rate by reducing the stochastic variance and the convergence rates with the proposed importance sampling methods can be significantly improved under suitable conditions both for prox-SGD and for proxies-SDCA.
- Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction391
The proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively.
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- Scaling Diffusion Language Models via Adaptation from Autoregressive Models299
A suite of DLMs capable of generating fluent text, performing in-context learning, filling in the middle without prompt re-ordering, and following instructions is released, revealing that these models outperform earlier DLMs and are competitive with their AR counterparts.
- Online Feature Selection and Its Applications282
This article investigates the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features, and presents novel algorithms to solve each of the two problems.
- Adversarial Sparse Transformer for Time Series Forecasting265
Adversarial Sparse Transformer (AST) is proposed, a new time series forecasting model based on Generative Adversarial Networks (GANs), based on the ground-truth provided during training and replaced by the network’s own one-step ahead output during inference during inference.
- Hierarchical graph learning for protein–protein interaction243
HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely and is a domain-knowledge-driven and interpretable framework for PPI prediction studies.
- Online AUC Maximization241
This work develops online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions, and presents two algorithms for online AUC maximization with theoretic performance guarantee.
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- LIBOL: a library for online learning algorithms236
LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large -scale online classification tasks.
- Online ARIMA Algorithms for Time Series Prediction216
This paper proposes online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency.
- Online Transfer Learning205
This work proposes a novel machine learning framework called "Online Transfer Learning" (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain, and investigates two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) O TL across heterogeneous domainsof different feature spaces.
- Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis195
This work innovatively proposes a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way and theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
- Online multimodal deep similarity learning with application to image retrieval175
This paper proposes a novel framework of online multimodal deep similarity learning (OMDSL), which aims to optimally integrate multiple deep neural networks pretrained with stacked denoising autoencoder to improve similarity search in multimedia information retrieval tasks.
- RetroXpert: Decompose Retrosynthesis Prediction like A Chemist152
A novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach Retrosynthesis prediction is devised, outperforming the state-of-the-art baselines by a significant margin and provides chemically reasonable interpretation.
- Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies149
A Margin Learning Embedded Prediction (MLEP) framework that helps with the detection of abnormal events, even for anomalies that have never been previously observed, and pushes the application of anomaly detection towards real scenarios.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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