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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

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828
3,646
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2000
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Papers (including subtopics)

Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks EMNLP 2018 Isolating Sources of Disentanglement in Variational Autoencoders NeurIPS 2018 Is Q-Learning Provably Efficient? NeurIPS 2018 ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing CVPR 2018 Iterate Averaging as Regularization for Stochastic Gradient Descent COLT 2018 Iterative Amortized Inference ICML 2018 Iterative Supervised Principal Components AISTATS 2018 ITER: Improving Translation Edit Rate through Optimizable Edit Costs EMNLP 2018 Japanese Predicate Conjugation for Neural Machine Translation NAACL 2018 Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator Interspeech 2018 Jointly Learning Network Connections and Link Weights in Spiking Neural Networks IJCAI 2018 Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank COLING 2018 Joint Neural Entity Disambiguation with Output Space Search COLING 2018 Joint Noise and Reverberation Adaptive Learning for Robust Speaker DOA Estimation with an Acoustic Vector Sensor Interspeech 2018 Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension ACL 2018 Kalman Normalization: Normalizing Internal Representations Across Network Layers NeurIPS 2018 Katyusha: The First Direct Acceleration of Stochastic Gradient Methods JMLR 2018 Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization ICML 2018 K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning ICML 2018 Kernel Density Estimation for Dynamical Systems JMLR 2018 Kernel Implicit Variational Inference ICLR 2018 Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor JMLR 2018 Kernel Recursive ABC: Point Estimation with Intractable Likelihood ICML 2018 Kronecker Recurrent Units ICML 2018 L4: Practical loss-based stepsize adaptation for deep learning NeurIPS 2018