neural network training

A Deep Learning Dataloader with Shared Data Preparation NeurIPS 2022 A Differential Entropy Estimator for Training Neural Networks ICML 2022 AI Snap! Blocks for Speech Input and Output, Computer Vision, Word Embeddings, and Neural Net Creation, Training, and Use AAAI 2022 AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness NeurIPS 2022 Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability NeurIPS 2022 A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions JMLR 2022 A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty CVPR 2022 A Stochastic Momentum Accelerated Quasi-Newton Method for Neural Networks (Student Abstract) AAAI 2022 A Unified Framework for Implicit Sinkhorn Differentiation CVPR 2022 A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks ICML 2022 Batch Normalization Preconditioning for Neural Network Training JMLR 2022 Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks AAAI 2022 Chaotic Dynamics are Intrinsic to Neural Network Training with SGD NeurIPS 2022 Characterizing Datapoints via Second-Split Forgetting NeurIPS 2022 Dataset Distillation using Neural Feature Regression NeurIPS 2022 Deep Learning in Target Space JMLR 2022 DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems AAAI 2022 Does the Data Induce Capacity Control in Deep Learning? ICML 2022 Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation NAACL 2022 Efficient Sharpness-aware Minimization for Improved Training of Neural Networks ICLR 2022 Exploiting Invariance in Training Deep Neural Networks AAAI 2022 Feature Learning and Signal Propagation in Deep Neural Networks ICML 2022 Gradient Descent on Neurons and its Link to Approximate Second-order Optimization ICML 2022 Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit NeurIPS 2022 Injecting Logical Constraints into Neural Networks via Straight-Through Estimators ICML 2022