neural network generalization

Probing Neural Network Generalization using Default Patterns NAACL 2025 Defog Artificial Intelligence Glasses: Neural Networks for the Imperfect Real World AAAI 2024 Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting AAAI 2024 PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion AAAI 2024 The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions NeurIPS 2024 Explicit Regularization in Overparametrized Models via Noise Injection AISTATS 2023 Improve Video Representation with Temporal Adversarial Augmentation IJCAI 2023 MixupE: Understanding and improving Mixup from directional derivative perspective UAI 2023 When Neural Networks Fail to Generalize? A Model Sensitivity Perspective AAAI 2023 Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule JMLR 2023 CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations EMNLP 2022 PAC-Bayes Information Bottleneck ICLR 2022 Self-Guidance: Improve Deep Neural Network Generalization via Knowledge Distillation WACV 2022 Spectral Bias in Practice: The Role of Function Frequency in Generalization NeurIPS 2022 Task Discovery: Finding the Tasks that Neural Networks Generalize on NeurIPS 2022 Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity ICLR 2021 Compositionality Decomposed: How do Neural Networks Generalise? (Extended Abstract) IJCAI 2020 Environmental drivers of systematicity and generalization in a situated agent ICLR 2020 Exactly Computing the Local Lipschitz Constant of ReLU Networks NeurIPS 2020 Rethinking Bias-Variance Trade-off for Generalization of Neural Networks ICML 2020 On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions CVPR 2019 Modern Neural Networks Generalize on Small Data Sets NeurIPS 2018