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Methodology
← Optimization & Theory
Machine Learning
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Optimization & Theory
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Loss Functions
1162 directly classified papers
Papers per year
2004: 1
2005: 1
2006: 3
2007: 4
2008: 3
2009: 5
2010: 7
2011: 11
2012: 11
2013: 8
2014: 15
2015: 18
2016: 16
2017: 30
2018: 57
2019: 124
2020: 120
2021: 165
2022: 140
2023: 174
2024: 111
2025: 106
2026: 32
Papers
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
IJCNLP 2021
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding
IJCNLP 2021
Embracing Ambiguity: Shifting the Training Target of NLI Models
IJCNLP 2021
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
EMNLP 2020
Semantic Label Smoothing for Sequence to Sequence Problems
EMNLP 2020
Proxy Anchor Loss for Deep Metric Learning
CVPR 2020
Minimax Classification with 0-1 Loss and Performance Guarantees
NIPS 2020
Rescuing neural spike train models from bad MLE
NIPS 2020
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
NIPS 2020
From Predictions to Decisions: Using Lookahead Regularization
NIPS 2020
Escaping the Gravitational Pull of Softmax
NIPS 2020
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
NIPS 2020
Deep Smoothing of the Implied Volatility Surface
NIPS 2020
Robust Deep Ordinal Regression under Label Noise
ACML 2020
Learning Interpretable Models using Soft Integrity Constraints
ACML 2020
DR Loss: Improving Object Detection by Distributional Ranking
CVPR 2020
Unsupervised Metric Relocalization Using Transform Consistency Loss
CORL 2020
Regularization via Structural Label Smoothing
AISTATS 2020
Calibrated Surrogate Losses for Adversarially Robust Classification
COLT 2020
Understanding the Disharmony between Weight Normalization Family and Weight Decay
AAAI 2020
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
AAAI 2020
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
AAAI 2020
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
ICML 2020
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
AISTATS 2020
Integrating Relation Constraints with Neural Relation Extractors
AAAI 2020
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