generalization performance

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation NeurIPS 2022 Redundant representations help generalization in wide neural networks NeurIPS 2022 Understanding Benign Overfitting in Gradient-Based Meta Learning NeurIPS 2022 Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search NeurIPS 2022 When Do Flat Minima Optimizers Work? NeurIPS 2022 A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization AAAI 2021 Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach EMNLP 2021 Disentangled Information Bottleneck AAAI 2021 EventDrop: Data Augmentation for Event-based Learning IJCAI 2021 Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions JMLR 2021 Noisy Concurrent Training for Efficient Learning Under Label Noise WACV 2021 On the geometry of generalization and memorization in deep neural networks ICLR 2021 Rate-Regularization and Generalization in Variational Autoencoders AISTATS 2021 Saliency is a Possible Red Herring When Diagnosing Poor Generalization ICLR 2021 Statistically robust neural network classification UAI 2021 Taxonomizing local versus global structure in neural network loss landscapes NeurIPS 2021 Understanding and Visualizing Generalization in UNets MIDL 2021 When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC ICML 2021 A Diversity-aware Model for Majority Vote Ensemble Accuracy AISTATS 2020 Curvature-corrected learning dynamics in deep neural networks ICML 2020 Distributed Kernel Ridge Regression with Communications JMLR 2020 Learning Augmentation Network via Influence Functions CVPR 2020 Learning to Select Base Classes for Few-Shot Classification CVPR 2020 MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures NeurIPS 2020 Small Data, Big Decisions: Model Selection in the Small-Data Regime ICML 2020