Loss Functions

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks ICML 2018 Hinge-Minimax Learner for the Ensemble of Hyperplanes JMLR 2018 Improving GAN Training via Binarized Representation Entropy (BRE) Regularization ICLR 2018 Improving Regression Performance with Distributional Losses ICML 2018 Investigations on Data Augmentation and Loss Functions for Deep Learning Based Speech-Background Separation Interspeech 2018 Large Margin Neural Language Model EMNLP 2018 Learning to Teach with Dynamic Loss Functions NeurIPS 2018 Learning towards Minimum Hyperspherical Energy NeurIPS 2018 Learning with Complex Loss Functions and Constraints AISTATS 2018 Loss Functions for Multiset Prediction NeurIPS 2018 Maximum-Entropy Fine Grained Classification NeurIPS 2018 Measuring the Diversity of Automatic Image Descriptions COLING 2018 Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics CVPR 2018 Pathologies of Neural Models Make Interpretations Difficult EMNLP 2018 Quantifying Learning Guarantees for Convex but Inconsistent Surrogates NeurIPS 2018 Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm (Extended Abstract) IJCAI 2018 Reducing Network Agnostophobia NeurIPS 2018 Regularization Learning Networks: Deep Learning for Tabular Datasets NeurIPS 2018 Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation ACL 2018 Regularizing Deep Networks by Modeling and Predicting Label Structure CVPR 2018 Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method IJCAI 2018 Repulsion Loss: Detecting Pedestrians in a Crowd CVPR 2018 Rethinking Feature Distribution for Loss Functions in Image Classification CVPR 2018 Ring Loss: Convex Feature Normalization for Face Recognition CVPR 2018 Sequential prediction with coded side information under logarithmic loss ALT 2018