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Works102 from public data
- Why ResNet Works? Residuals Generalize410
According to the obtained generalization bound, regularization terms should be introduced to control the magnitude of the norms of weight matrices not to increase too much, in practice, to ensure a good generalization ability, which justifies the technique of weight decay.
- Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence247
A PAC-Bayes generalization bound for neural networks trained by SGD is proved, which has a positive correlation with the ratio of batch size to learning rate, which builds the theoretical foundation of the training strategy.
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- DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training186
This work proposes a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge to save the communication and computation cost.
- Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning164
A Semantic Alignment Metric Learning (SAML) method for few-shot learning that aligns the semantically relevant dominant objects through a ``collect-and-select'' strategy and demonstrates the generalization ability of SAML and gives a theoretical guarantee.
- Fast Spatio-Temporal Residual Network for Video Super-Resolution156
A novel fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the video SR task in order to enhance the performance while maintaining a low computational load and significantly outperforms the current state-of-the-art methods.
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- Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting75
A neural variable risk minimization framework and neural variable optimizers are devised to achieve ANV for conventional network architectures in practice, and the empirical studies demonstrate that NVRM can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
- Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition69
The first attempt to perform textual reasoning based on visual semantics in this paper is made by paralleling GTR to the language model in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning.
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- Topology-aware Generalization of Decentralized SGD60
The theory indicates that the generalizability of D-SGD is positively correlated with the spectral gap, and can explain why consensus control in initial training phase can ensure better generalization.
- Bridged Transformer for Vision and Point Cloud 3D Object Detection58
Bridged Transformer is proposed, an end-to-end architecture for 3D object detection which learns to identify 3D and 2D object bounding boxes from both points and image patches and surpasses state-of-the-art methods on SUN RGB-D and ScanNetV2 datasets.
- Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction53
Channel-Exchanging-Network (CEN) is proposed which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction and adaptively exchanges channels between subnetworks of different modalities.
- 53
- Robust Unlearnable Examples: Protecting Data Against Adversarial Learning52
Experiments show that the unlearnability brought by robust error-minimizing noise can effectively protect data from adversarial training in various scenarios.
- InFi44
This work proposes the first end-to-end learnable input filtering framework that covers most state-of-the-art methods and surpasses them in feature embedding with robust discriminability and designs and implements an input filtering system InFi, the first to support text and sensor signal inputs and model partitioning deployments widely adopted by under-resourced mobile systems.
- Siamese Network with Interactive Transformer for Video Object Segmentation43
A novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames and to extract backbone features of both past and current frames, which enables feature reuse and is more efficient than existing methods.
- Knowledge Removal in Sampling-based Bayesian Inference39
This paper proposes the first machine unlearning algorithm for Markov chain Monte Carlo (MCMC), designed to provably characterize the learned knowledge from data, and shows that MCMC unlearning would not compromise the generalizability of the MCMC models.
- Understanding Generalization in Recurrent Neural Networks35
This work proposes to add random noise to the input data and proves a generalization bound for training with random noise, which is an extension of the former one, and discovers that Fisher-Rao norm for RNNs can be interpreted as a measure of gradient, and incorporating this gradient measure can tighten the bound.
- $E(2)$-Equivariant Vision Transformer33
This paper designs a Group Equivariant Vision Transformer via a novel, effective positional encoding operator, and proves that GE-ViT meets all the theoretical requirements of an equivariant neural network.
- Understanding Deep Learning via Decision Boundary29
It is discovered that the neural network (NN) with lower decision boundary (DB) variability has better generalizability and an upper bound of order is proved which is convenient to estimate without the requirement of labels and does not explicitly depend on the network size which is usually prohibitively large in deep learning.
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- Self-Ensembling GAN for Cross-Domain Semantic Segmentation26
Despite its simplicity, this paper finds SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model, the latter of which is a common barrier shared by most adversarialTraining-based methods.
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- Decentralized SGD and Average-direction SAM are Asymptotically Equivalent23
It is proved that D-SGD implicitly minimizes the loss function of an average-direction Sharpness-aware minimization (SAM) algorithm under general non-convex non-$\beta$-smooth settings, and this surprising asymptotic equivalence reveals an intrinsic regularization-optimization trade-off and three advantages of decentralization.
Publication data from OpenAlex; 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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