Keywords

Keywords have to be chosen during the paper submission process. The purpose of the keywords is to assist the assignment of papers to area chairs and reviewers. They are sorted alphabetically.

Active Learning
Adversarial Networks
Attention Models
Audio and Speech Processing
Auditory Perception and Modeling
Bandit Algorithms
Bayesian Nonparametrics
Bayesian Theory
Belief Propagation
Benchmarks
Biologically Plausible Deep Networks
Boosting and Ensemble Methods
Brain--Computer Interfaces and Neural Prostheses
Brain Imaging
Brain Mapping
Brain Segmentation
Causal Inference
Classification
Clustering
Cognitive Science
Collaborative Filtering
Combinatorial Optimization
Competitions or Challenges
Competitive Analysis
Components Analysis (e.g., CCA, ICA, LDA, PCA)
Computational Biology and Bioinformatics
Computational Complexity
Computational Social Science
Computer Vision
Connectomics
Control Theory
Convex Optimization
Data Sets or Data Repositories
Decision and Control
Deep Autoencoders
Denoising
Density Estimation
Dialog- and/or Communication-Based Learning
Distributed Inference
Dynamical Systems
Efficient Inference Methods
Efficient Training Methods
Embedding Approaches
Exploration
Fairness, Accountability, and Transparency
Frequentist Statistics
Game Playing
Game Theory and Computational Economics
Gaussian Processes
Generative Models
Graphical Models
Hardness of Learning and Approximations
Hardware and Systems
Hierarchical Models
Hierarchical RL
Human or Animal Learning
Hyperparameter Selection
Image Segmentation
Information Retrieval
Information Theory
Interaction-Based Deep Networks
Kernel Methods
Language for Cognitive Science
Large Deviations and Asymptotic Analysis
Large Margin Methods
Latent Variable Models
Learning Theory
Learning to Learn
Markov Decision Processes
Matrix and Tensor Factorization
MCMC

Memory
Memory-Augmented Neural Networks
Metric Learning
Missing Data
Model-Based RL
Model Selection and Structure Learning
Motor Control
Multi-Agent RL
Multitask and Transfer Learning
Music Modeling and Analysis
Natural Language Processing
Natural Scene Statistics
Navigation
Network Analysis
Neural Abstract Machines
Neural Coding
Neuropsychology
Neuroscience
Non-Convex Optimization
Nonlinear Dimensionality Reduction and Manifold Learning
Object Detection
Object Recognition
One-Shot/Low-Shot Learning Approaches
Online Learning
Optimization for Deep Networks
Perception
Planning
Plasticity and Adaptation
Predictive Models
Privacy, Anonymity, and Security
Problem Solving
Program Induction
Quantitative Finance and Econometrics
Ranking and Preference Learning
Reasoning
Recommender Systems
Recurrent Networks
Regression
Regularization
Reinforcement Learning
Relational Learning
Representation Learning
Robotics
Semi-Supervised Learning
Signal Processing
Similarity and Distance Learning
Software Toolkits
Source Separation
Spaces of Functions and Kernels
Sparse Coding and Dimensionality Expansion
Sparsity and Compressed Sensing
Spectral Methods
Speech Recognition
Spike Train Generation
Statistical Physics of Learning
Stochastic Methods
Structured Prediction
Submodular Optimization
Supervised Deep Networks
Synaptic Modulation
Systems Biology
Text Analysis
Time Series Analysis
Topic Models
Unsupervised Learning
Variational Inference
Video, Motion and Tracking
Virtual Environments
Visual Features

Visualization/Expository Techniques for Deep Networks
Visual Perception
Visual Question Answering
Visual Scene Analysis and Interpretation
Web Applications and Internet Data