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- 3,499
- SPIDER and WEB: Processing and Visualization of Images in 3D Electron Microscopy and Related Fields2,145
Novel features are a suite of operations relating to the determination, modeling, and correction of the contrast transfer function and the availability of the entire documentation in hypertext format.
- A Comprehensive Survey of Continual Learning: Theory, Method and Application1,672
This work presents a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications, and summarizes the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency.
- Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks1,441
A spatio-temporal backpropagation (STBP) algorithm for training high-performance SNNs is proposed, which combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill.
- DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models1,172
DPM-Solver++ is proposed, a high-order solver for the guided sampling of DPMs that solves the diffusion ODE with the data prediction model and adopts thresholding methods to keep the solution matches training data distribution and can generate high-quality samples within only 15 to 20 steps.
- Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks1,164
A translation-invariant attack method to generate more transferable adversarial examples against the defense models, which fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the current defense techniques.
- Pre-trained models: Past, present and future1,134
A deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, is taken to reveal the crucial position of PTMs in the AI development spectrum.
- Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser1,059
High-level representation guided denoiser (HGD) is proposed as a defense for image classification by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image.
- Direct Training for Spiking Neural Networks: Faster, Larger, Better824
This work proposes a neuron normalization technique to adjust the neural selectivity and develops a direct learning algorithm for deep SNNs and presents a Pytorch-based implementation method towards the training of large-scale Snns.
- Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges693
The history of Explainable AI is introduced, starting from expert systems and traditional machine learning approaches to the latest progress in the context of modern deep learning, and the major research areas and the state-of-art approaches in recent years are described.
- 630
- Stochastic Training of Graph Convolutional Networks with Variance Reduction576
Control variate based algorithms which allow sampling an arbitrarily small neighbor size are developed and a new theoretical guarantee for these algorithms to converge to a local optimum of GCN is proved.
- Towards Better Analysis of Deep Convolutional Neural Networks524
A hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them and a biclustering-based edge bundling method is proposed to reduce visual clutter caused by a large number of connections between neurons.
- GNOT: A General Neural Operator Transformer for Operator Learning506
This work proposes a general neural operator transformer (GNOT), a scalable and effective transformer-based framework for learning operators that is highly flexible to handle multiple input functions and irregular meshes and introduces a geometric gating mechanism which could be viewed as a soft domain decomposition to solve the multi-scale problems.
- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition478
This paper evaluates the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
- Towards better analysis of machine learning models: A visual analytics perspective399
This paper presents a comprehensive analysis and interpretation of interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization with a focus on big data analytics.
- Adversarial Attacks and Defences Competition364
In this chapter, the structure and organization of the NIPS 2017 competition is described and the solutions developed by several of the top-placing teams are described.
- 308
- StatSnowball304
A statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE, is proposed and a working entity relation search engine called Renlifang is developed based on it.
- Towards a new generation of artificial intelligence in China285
This Perspective briefly introduces the New Generation Artificial Intelligence Development Plan of China (2015–2030) from the point of view of the authors, a group of AI experts from academia and industry who have been involved in various stages of the plan.
- Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning265
A novel method, called Smooth Neighbors on Teacher Graphs (SNTG), which serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold and achieves state-of-the-art results on semi-supervised learning benchmarks.
- MedLDA254
The approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction.
- Cluster Alignment With a Teacher for Unsupervised Domain Adaptation248
Cluster Alignment with a Teacher (CAT) is proposed for unsupervised domain adaptation, which can effectively incorporate the discriminative clustering structures in both domains for better adaptation.
- Simultaneous record detection and attribute labeling in web data extraction208
It is shown that separately extracting data records and attributes is highly ineffective and a probabilistic model to perform these two tasks simultaneously is proposed and it can also incorporate hierarchical interactions which are very important for Web data extraction.
- Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications207
This survey presents a model that leverages empirical data and available physical prior knowledge to improve performance on a set of tasks that involve a physical mechanism and argues that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications is far from being fully explored.
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- Sparse Adversarial Perturbations for Videos168
An l2,1-norm based optimization algorithm is proposed to compute the sparse adversarial perturbations for videos and chooses the action recognition as the targeted task, and networks with a CNN+RNN architecture as threat models to verify the method.
- 160
- Bayesian inference with posterior regularization and applications to infinite latent SVMs160
Regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation, is presented.
- 156
- Improving Transferability of Adversarial Patches on Face Recognition with Generative Models150
This paper extends the existing transfer-based attack techniques and proposes to regularize the adversarial patches on the low dimensional data manifold to show that the gaps between the responses of substitute models and the target models dramatically decrease, exhibiting a better transferability.
- 147
- OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization145
This work first identifies and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets, and compares OoD generalization algorithms across two groups of benchmarks, revealing their strengths on one shift as well as limitations on the other shift.
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- 2D Conditional Random Fields for Web information extraction142
This paper presents a two-dimensional CRF model to automatically extract object information from the Web, and empirically compare the proposed model with existing linear-chain CRF models for product information extraction, and the results show the effectiveness of the model.
- Discriminative Deep Random Walk for Network Classification136
This paper presents Discriminative Deep Random Walk (DDRW), a novel method for relational network classification that significantly outperforms DeepWalk on multilabel network classification tasks, while retaining the topological structure in the latent space.
- Improving Interpretability of Deep Neural Networks with Semantic Information134
This work proposes a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions by extracting a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts.
- 128
- Towards Face Encryption by Generating Adversarial Identity Masks127
This work proposes a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality.
- ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning115
This work considers semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data, and proposes deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier.
- Learning to Write Stylized Chinese Characters by Reading a Handful of Examples114
A novel framework of Style-Aware Variational Auto-Encoder (SA-VAE), which disentangles the content-relevant and style-relevant components of a Chinese character feature with a novel intercross pair-wise optimization method and has a powerful one-shot/few-shot generalization ability.
- Incorporating neuro-inspired adaptability for continual learning in artificial intelligence112
By modelling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, this work proposes a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility.
- Generative Topic Embedding: a Continuous Representation of Documents112
A generative topic embedding model is proposed that performs better than eight existing methods, with fewer features, and can generate coherent topics even based on only one document.
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- Analyzing the Noise Robustness of Deep Neural Networks106
A visual analysis method to explain why adversarial examples are misclassified is presented and a multi-level visualization has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process.
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- Visual Diagnosis of Tree Boosting Methods99
A visual diagnosis tool to help experts quickly analyze and diagnose the training process of tree boosting, and designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization to provide a comprehensive overview of a tree boosting model.
- A Hierarchical Recurrent Neural Network for Symbolic Melody Generation96
A hierarchical recurrent neural network for melody generation, which consists of three long-short-term-memory (LSTM) subnetworks working in a coarse-to-fine manner along time, which produces better melodies evaluated by humans.
- Big Learning with Bayesian methods94
A survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian Methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.
- LiBRe: A Practical Bayesian Approach to Adversarial Detection93
This work builds the few-layer deep ensemble variational and adopt the pre-training & fine-tuning workflow to boost the effectiveness and efficiency of LiBRe, and provides a novel insight to realise adversarial detection-oriented uncertainty quantification without inefficiently crafting adversarial examples during training.
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- Incorporating site-level knowledge to extract structured data from web forums89
The target is to find a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site, by incorporating both page-level and site-level knowledge and employing Markov logic networks.
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- Learning from crowds in the presence of schools of thought78
This work presents a statistical model to estimate worker reliability and task clarity without resorting to the single gold standard assumption, instantiated by explicitly characterizing the grouping behavior to form schools of thought with a rank-1 factorization of a worker-task groupsize matrix.
- Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition75
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems, and designs adversarial textured 3D meshes with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses.
- WarpLDA75
WarpLDA is developed, an LDA sampler which achieves both the best O(1) time complexity per token and thebest O(K) scope of random access, within a short period of time.
- Detection Transformer with Stable Matching73
This paper proposes two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost, and shows consistent improvements over baselines.
- Forecast the Plausible Paths in Crowd Scenes73
A social-aware recurrent Gaussian process model is derived to explore the inherent crowd dynamics via asocial-aware LSTM to explored the crowd dynamic, resulting in a hidden feature embedding the rich prior in massive data.
- Scaling up Dynamic Topic Models73
This paper presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradient Langevin Dynamics that does not make any unwarranted assumptions and is able to learn the largest Dynamic Topic Model to the authors' knowledge.
- Scalable Inference for Logistic-Normal Topic Models71
This paper presents a partially collapsed Gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation and presents a parallel implementation that can deal with large-scale applications and learn the correlation structures of thousands of topics from millions of documents.
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- Crowd Fraud Detection in Internet Advertising67
This paper carefully examines the characteristics of the group behaviors of crowd fraud and identifies three persistent patterns, which are moderateness, synchronicity and dispersivity, and proposes an effective crowd fraud detection method for search engine advertising based on these patterns.
- Triple-Memory Networks: A Brain-Inspired Method for Continual Learning66
A novel approach named triple memory networks (TMNs) for continual learning that model the interplay of hippocampus, prefrontal cortex and sensory cortex as a triple-network architecture of generative adversarial networks (GAN) and achieves new state-of-the-art performance on a variety of class-incremental learning benchmarks.
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- Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features63
This paper proposes a human parsing pipeline that uses pose cues, e.g., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts, and evaluates the approach on the popular Penn-Fudan pedestrian parsing dataset.
- Maximum Entropy Discrimination Markov Networks61
This paper presents a new general framework called maximum entropy discrimination Markov networks (MaxEnDNet), which integrates these two approaches and combines and extends their merits, and demonstrates that empirically it outperforms a wide array of competing methods for structured input/output learning on both synthetic and real OCR and web data extraction data sets.
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- PopMNet: Generating structured pop music melodies using neural networks57
A melody structure-based model called PopMNet is presented to generate structured pop music melodies that contain much clearer structures compared to those generated by other models, as confirmed by human behavior experiments.
- Distributed Bayesian Posterior Sampling via Moment Sharing57
A distributed Markov chain Monte Carlo inference algorithm for large scale Bayesian posterior simulation that improves posterior estimation quality by enforcing agreement among the samplers and demonstrating the speed and inference quality with empirical studies on Bayesian logistic regression and sparse linear regression.
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- Training Interpretable Convolutional Neural Networks by Differentiating Class-Specific Filters53
Inspired by cellular differentiation, this work proposes a novel strategy to train interpretable CNNs by encouraging class-specific filters, among which each filter responds to only one (or few) class.
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- Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks52
A conditional generative attacking model is proposed, which can generate the adversarial examples targeted at different classes by simply altering the class embedding and share a single backbone, which improves the success rates of targeted black-box attacks by a significant margin over the existing methods.
- Stochastic Expectation Maximization with Variance Reduction52
It is shown that sEM-vr has the same exponential asymptotic convergence rate as batch EM, and only requires a constant step size to achieve this rate, which alleviates the burden of parameter tuning.
- Design and Interpretation of Universal Adversarial Patches in Face Detection51
This work investigates a phenomenon: patches designed to suppress real face detection appear face-like, which holds generally across different initialization, locations, scales of patches, backbones, and state-of-the-art face detection frameworks.
- Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process51
A novel framework of Social-Aware Generative Adversarial Imitation Learning (SA-GAIL) is proposed to mimic the underlying decision-making process of pedestrians in crowds and infer the latent factors of human decision- making process in an unsupervised manner.
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- Gibbs Max-Margin Topic Models with Fast Sampling Algorithms50
By introducing augmented variables, this paper develops simple and fast Gibbs sampling algorithms with no restricting assumptions and no need to solve SVM subproblems for both classification and regression.
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- Webpage understanding47
This paper proposes to segment and label the page structure and the text content of a webpage in a joint discriminative probabilistic model, and semantic labels of page structure can be leveraged to help text content understanding, and semantic labels of the text phrases can be used in page structure understanding tasks such as data record detection.
- Max-Margin Deep Generative Models for (Semi-)Supervised Learning46
Max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability.
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- Crowd scene understanding with coherent recurrent neural networks46
A Coherent Long Short Term Memory (cLSTM) network is presented to capture the nonlinear crowd dynamics by learning an informative representation of crowd motions, which facilitates the critical tasks in crowd scene analysis.
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- Unsupervised Part Segmentation through Disentangling Appearance and Shape42
A bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance, and combined with a self-supervised part classification loss and an improved geometry concentration constraint can segment more consistent parts with semantic meanings.
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- Fast Max-Margin Matrix Factorization with Data Augmentation41
This paper presents a probabilistic M3F model that admits a highly efficient Gibbs sampling algorithm through data augmentation and extends the approach to incorporate Bayesian nonparametrics and build accordingly a truncation-free nonparametric M3f model where the number of latent factors is literally unbounded and inferred from data.
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- Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models40
This work unifies FOL and Bayesian modeling under the regularized Bayesian framework by imposing First-Order Logic rules on the posterior distribution and automatically estimates the uncertainty of FOL rules when they are produced by humans, so that reliable rules are incorporated while unreliable ones are ignored.
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- Dropout Training for Support Vector Machines39
An iteratively re-weighted least square (IRLS) algorithm is developed by exploring data augmentation techniques to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions.
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- Detecting Semantic Parts on Partially Occluded Objects38
Experiments show that the algorithm outperforms several competitors in semantic part detection when occlusions are present, and uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues.
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- Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning36
This work presents a hierarchical model that works in a manager-worker fashion over two levels of hierarchy that significantly outperforms the alternative RL-based models on FPS games requiring maze solving and combat skills, etc.
- Discriminative Relational Topic Models35
Three extensions of the generic RegBayes framework are presented, which perform regularized Bayesian inference with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations.
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- Collaborative Filtering With User-Item Co-Autoregressive Models34
This work proposes CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items, and develops an efficient stochastic learning algorithm to handle large scale datasets.
- Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields34
Gated Recursive Semi- CRFs (grSemi-CRFs), which model segments directly and automatically learn segmentlevel features through a gated recursive convolutional neural network, and generally outperform other neural models.
- Revisit Long Short-Term Memory: An Optimization Perspective34
This work proposes a matrix-based batch learning method for LSTM with full Backpropagation Through Time (BPTT) and solves the state drifting issues as well as improving the overall performance for L STM using revised activation functions for gates.
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- Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart28
This work finds that confidence and a rectified confidence can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones, and sheds light on using coupling strategies to better detect and reject adversarial examples.
- TarGAN: Generating target data with class labels for unsupervised domain adaptation28
A new unsupervised domain adaptation method named TarGAN is proposed, which can successfully disentangle the class and the style codes of the target generator and generate Target samples with given class labels based on the recent advances of Generative Adversarial Networks.
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- Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference21
In this paper, the variance reduction tricks on Hamiltonian Monte Carlo are applied and better theoretical convergence results compared with the variance-reduced Langevin dynamics are achieved and the experimental results are also consistent with the theoretical results.
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- Face3DAdv: Exploiting Robust Adversarial 3D Patches on Physical Face Recognition13
By reassessing the intrinsic relationship between an attacker’s face and its variations, a practical pipeline is proposed that simulates complex facial transformations in the physical world through 3D face modeling and can achieve an effective evaluation of adversarial robustness on multiple popular commercial services.
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- Special Issue on Biomedical Big Data: Understanding, Learning and Applications6
The papers in this special issue focus on biomedical Big Data, where the main challenge and bottleneck for the related research is the conversion of “biomedical big data” into interpretable information and hence discoveries.
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- Heterogeneous multi-task Gaussian Cox processes5
To circumvent the non-conjugate Bayesian inference in the MOGP modulated heterogeneous multi-task framework, the data augmentation technique is employed and derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters.
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- DeGMix: Efficient Multi-Task Dense Prediction With Deformable and Gating Mixer2
DeGMix is proposed, a unified encoder–decoder framework for multi-task dense prediction that combines deformable convolution and attention-based task interaction that consistently outperforms competitive CNN-based and Transformer-based multi-task methods across multiple dense prediction tasks.
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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 2026-10-13. 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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