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Loss Functions

1,162 papers

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Papers

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity JMLR 2018
Surprising properties of dropout in deep networks JMLR 2018
Hinge-Minimax Learner for the Ensemble of Hyperplanes JMLR 2018
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities SEMEVAL 2018
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NIPS 2017
Variance-based Regularization with Convex Objectives NIPS 2017
Learning with Average Top-k Loss NIPS 2017
Stabilizing Training of Generative Adversarial Networks through Regularization NIPS 2017
Robust Estimation of Neural Signals in Calcium Imaging NIPS 2017
Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures ACL 2017
Probability Calibration Trees ACML 2017
Value-Aware Loss Function for Model-based Reinforcement Learning AISTATS 2017
A Strongly Quasiconvex PAC-Bayesian Bound ALT 2017
Multi-Observation Elicitation COLT 2017
Loss Max-Pooling for Semantic Image Segmentation CVPR 2017
BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks CVPR 2017
Improving Pairwise Ranking for Multi-Label Image Classification CVPR 2017
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective EMNLP 2017
Soft-DTW: a Differentiable Loss Function for Time-Series ICML 2017
On Calibration of Modern Neural Networks ICML 2017
Confident Multiple Choice Learning ICML 2017
Forest-type Regression with General Losses and Robust Forest ICML 2017
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling ICML 2017
Regularising Non-linear Models Using Feature Side-information ICML 2017
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer ICML 2017