gradient variance

How Sampling Impacts the Robustness of Stochastic Neural Networks NeurIPS 2022 Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes NeurIPS 2022 Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients NeurIPS 2022 The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models NeurIPS 2022 ARCH: Efficient Adversarial Regularized Training with Caching EMNLP 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent COLT 2021 Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation ICLR 2020 An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise AISTATS 2020 Augment Your Batch: Improving Generalization Through Instance Repetition CVPR 2020 Extreme Classification via Adversarial Softmax Approximation ICLR 2020 On the interplay between noise and curvature and its effect on optimization and generalization AISTATS 2020 The Break-Even Point on Optimization Trajectories of Deep Neural Networks ICLR 2020 Deterministic Variational Inference for Robust Bayesian Neural Networks ICLR 2019 Provable Gradient Variance Guarantees for Black-Box Variational Inference NeurIPS 2019 Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018 Not All Samples Are Created Equal: Deep Learning with Importance Sampling ICML 2018 Pathwise Derivatives Beyond the Reparameterization Trick ICML 2018 Using Large Ensembles of Control Variates for Variational Inference NeurIPS 2018