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Subject Areas

Authors must choose subject areas (one primary, multiple secondary) when they submit a paper. These subject areas help the program chairs to find the most appropriate reviewers for each submission.

∟Active Learning
∟Adaptive Data Analysis
∟Bandit Algorithms
∟Boosting and Ensemble Methods
∟Collaborative Filtering
∟Components Analysis (e.g., CCA, ICA, LDA, PCA)
∟Density Estimation
∟Dynamical Systems
∟Kernel Methods
∟Large Margin Methods
∟Metric Learning
∟Missing Data
∟Model Selection and Structure Learning
∟Multitask and Transfer Learning
∟Nonlinear Dimensionality Reduction and Manifold Learning
∟Online Learning
∟Ranking and Preference Learning
∟Relational Learning
∟Representation Learning
∟Semi-Supervised Learning
∟Similarity and Distance Learning
∟Sparse Coding and Dimensionality Expansion
∟Sparsity and Compressed Sensing
∟Spectral Methods
∟Stochastic Methods
∟Structured Prediction
∟Unsupervised Learning
∟Activity and Event Recognition
∟Audio and Speech Processing
∟Body Pose, Face, and Gesture Analysis
∟Communication- or Memory-Bounded Learning
∟Computational Biology and Bioinformatics
∟Computational Photography
∟Computational Social Science
∟Computer Vision
∟Dialog- or Communication-Based Learning
∟Fairness, Accountability, and Transparency
∟Game Playing
∟Hardware and Systems
∟Image Segmentation
∟Information Retrieval
∟Matrix and Tensor Factorization
∟Motor Control
∟Music Modeling and Analysis
∟Natural Language Processing
∟Natural Scene Statistics
∟Network Analysis
∟Object Detection
∟Object Recognition
∟Privacy, Anonymity, and Security
∟Quantitative Finance and Econometrics
∟Recommender Systems
∟Signal Processing
∟Source Separation
∟Speech Recognition
∟Systems Biology
∟Text Analysis
∟Time Series Analysis
∟Tracking and Motion in Video
∟Video Analysis
∟Video Segmentation
∟Visual Features
∟Visual Question Answering
∟Visual Scene Analysis and Interpretation
∟Web Applications and Internet Data
Data, Competitions, Implementations, and Software
∟Competitions or Challenges
∟Data Sets or Data Repositories
∟Software Toolkits
Deep Learning
∟Adversarial Networks
∟Attention Models
∟Biologically Plausible Deep Networks
∟CNN Architectures
∟Deep Autoencoders
∟Efficient Inference Methods
∟Efficient Training Methods
∟Embedding Approaches
∟Few-Shot Learning Approaches
∟Generative Models
∟Interaction-Based Deep Networks
∟Memory-Augmented Neural Networks
∟Neural Abstract Machines
∟Optimization for Deep Networks
∟Predictive Models
∟Program Induction
∟Recurrent Networks
∟Supervised Deep Networks
∟Virtual Environments
∟Visualization or Exposition Techniques for Deep Networks
Neuroscience and Cognitive Science
∟Auditory Perception
∟Brain Imaging
∟Brain Mapping
∟Brain Segmentation
∟Brain--Computer Interfaces and Neural Prostheses
∟Cognitive Science
∟Human or Animal Learning
∟Language for Cognitive Science
∟Neural Coding
∟Plasticity and Adaptation
∟Problem Solving
∟Spike Train Generation
∟Synaptic Modulation
∟Visual Perception
None of the Above
∟Combinatorial Optimization
∟Convex Optimization
∟Non-Convex Optimization
∟Submodular Optimization
Probabilistic Methods
∟Bayesian Nonparametrics
∟Bayesian Theory
∟Belief Propagation
∟Causal Inference
∟Distributed Inference
∟Gaussian Processes
∟Graphical Models
∟Hierarchical Models
∟Latent Variable Models
∟Topic Models
∟Variational Inference
Reinforcement Learning and Planning
∟Decision and Control
∟Hierarchical RL
∟Markov Decision Processes
∟Model-Based RL
∟Multi-Agent RL
∟Reinforcement Learning
∟Competitive Analysis
∟Computational Complexity
∟Control Theory
∟Frequentist Statistics
∟Game Theory and Computational Economics
∟Hardness of Learning and Approximations
∟Information Theory
∟Large Deviations and Asymptotic Analysis
∟Learning Theory
∟Spaces of Functions and Kernels
∟Statistical Physics of Learning