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Application Areas

31,658 papers (3 classified directly here)

Subtopics

Domain Adaptation (8,962) Efficient Computing (6,898) Data Augmentation (3,720) Fairness (3,511) Knowledge Distillation (3,086) Privacy (2,847) Domain Generalization (1,834) Model Compression (1,718) Risk Management (1,064) Model Merging (558) Recommender Systems (315) Information Retrieval (244) Text Classification (139) Text Processing (5)

Papers per year

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Papers (including subtopics)

Universally Quantized Neural Compression NIPS 2020
Investigating Gender Bias in Language Models Using Causal Mediation Analysis NIPS 2020
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients NIPS 2020
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators NIPS 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks NIPS 2020
Multi-Stage Influence Function NIPS 2020
Probabilistic Fair Clustering NIPS 2020
CogLTX: Applying BERT to Long Texts NIPS 2020
ConvBERT: Improving BERT with Span-based Dynamic Convolution NIPS 2020
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation NIPS 2020
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning NIPS 2020
CompRess: Self-Supervised Learning by Compressing Representations NIPS 2020
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps NIPS 2020
Instance Selection for GANs NIPS 2020
Sample Complexity of Uniform Convergence for Multicalibration NIPS 2020
Latent Bandits Revisited NIPS 2020
Neurosymbolic Transformers for Multi-Agent Communication NIPS 2020
Fairness in Streaming Submodular Maximization: Algorithms and Hardness NIPS 2020
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions NIPS 2020
Group-Fair Online Allocation in Continuous Time NIPS 2020
Understanding Gradient Clipping in Private SGD: A Geometric Perspective NIPS 2020
Transferable Graph Optimizers for ML Compilers NIPS 2020
Learning Bounds for Risk-sensitive Learning NIPS 2020
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency NIPS 2020
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NIPS 2020