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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Two-Player Games for Efficient Non-Convex Constrained Optimization ALT 2019 Two-stage Federated Phenotyping and Patient Representation Learning ACL 2019 Two-temperature logistic regression based on the Tsallis divergence AISTATS 2019 Two Tiered Distributed Training Algorithm for Acoustic Modeling Interspeech 2019 Two-Timescale Networks for Nonlinear Value Function Approximation ICLR 2019 Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples NeurIPS 2019 Ultra-Compact NLU: Neuronal Network Binarization as Regularization Interspeech 2019 UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs SemEval 2019 Unbiased Implicit Variational Inference AISTATS 2019 Unbiased Smoothing using Particle Independent Metropolis-Hastings AISTATS 2019 Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference ICCV 2019 Uncertainty-based Continual Learning with Adaptive Regularization NeurIPS 2019 Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining CVPR 2019 Uncertainty on Asynchronous Time Event Prediction NeurIPS 2019 Uncovering Probabilistic Implications in Typological Knowledge Bases ACL 2019 Uncovering Specific-Shape Graph Anomalies in Attributed Graphs AAAI 2019 Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework EMNLP 2019 Understanding and Accelerating Particle-Based Variational Inference ICML 2019 Understanding and Controlling Memory in Recurrent Neural Networks ICML 2019 Understanding and correcting pathologies in the training of learned optimizers ICML 2019 Understanding and Improving Layer Normalization NeurIPS 2019 Understanding and Visualizing Raw Waveform-Based CNNs Interspeech 2019 Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization EMNLP 2019 Understanding Geometry of Encoder-Decoder CNNs ICML 2019 Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation ICML 2019