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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.
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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