Loss Functions

Investigating Efficient Feature Representation Methods and Training Objective for BLSTM-Based Phone Duration Prediction Interspeech 2017 Learning with Average Top-k Loss NeurIPS 2017 Loss Max-Pooling for Semantic Image Segmentation CVPR 2017 Multi-Class Support Vector Machine via Maximizing Multi-Class Margins IJCAI 2017 Multi-Observation Elicitation COLT 2017 On Calibration of Modern Neural Networks ICML 2017 On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NeurIPS 2017 Probability Calibration Trees ACML 2017 Regularising Non-linear Models Using Feature Side-information ICML 2017 Robust Estimation of Neural Signals in Calcium Imaging NeurIPS 2017 Soft-DTW: a Differentiable Loss Function for Time-Series ICML 2017 Stabilizing Training of Generative Adversarial Networks through Regularization NeurIPS 2017 Training Word Sense Embeddings With Lexicon-based Regularization IJCNLP 2017 Uncorrelation and Evenness: a New Diversity-Promoting Regularizer ICML 2017 Value-Aware Loss Function for Model-based Reinforcement Learning AISTATS 2017 Variance-based Regularization with Convex Objectives NeurIPS 2017 Bipartite Ranking: a Risk-Theoretic Perspective JMLR 2016 Composite Multiclass Losses JMLR 2016 Convex Calibration Dimension for Multiclass Loss Matrices JMLR 2016 DisturbLabel: Regularizing CNN on the Loss Layer CVPR 2016 Extreme F-measure Maximization using Sparse Probability Estimates ICML 2016 Feature-Level Domain Adaptation JMLR 2016 Large-Margin Softmax Loss for Convolutional Neural Networks ICML 2016 Learning Activity Progression in LSTMs for Activity Detection and Early Detection CVPR 2016 Learning Deep Embeddings with Histogram Loss NeurIPS 2016