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NeurIPS 2019 – analysis of papers by themes

You can always learn a lot from the papers presented at NeurIPS

There is some good analysis already on the web.

From Chip Huygen – neurips 2019 analysis and from David Abel neurips 2019 analysis

Most major players were also well represented at NeurIPS including       

Facebook at neurips 2019

Microsoft at neurips 2019

Google at neurips 2019 

Intel at neurips 2019

Unity 3d at neurips 2019

IBM at neurips 2019

Apple at neurips 2019

For my students at University of Oxford #AI #Cloud #Edge I did an analysis of neurips papers by theme based on the neurips 2019 schedule. I found it easier to analyse papers based on theme

The themes covered in NeurIPS were

Algorithms

  • Adaptive Data Analysis
  • Adversarial Learning
  • Bandit Algorithms
  • Boosting and Ensemble Methods
  • Clustering
  • Components Analysis (e.g., CCA, ICA, LDA, PCA)
  • Density Estimation
  • Dynamical Systems
  • Kernel Methods
  • Meta-Learning                
  • Missing Data
  • Model Selection and Structure Learning                
  • Regression
  • Representation Learning
  • Semi-Supervised Learning
  • Similarity and Distance Learning
  • Structured Prediction
  • Uncertainty Estimation
  • Unsupervised Learning

Applications

  • Body Pose, Face, and Gesture Analysis
  • Communication- or Memory-Bounded Learning
  • Computer Vision                
  • Dialog- or Communication-Based Learning
  • Game Playing
  • Image Segmentation
  • Object Detection
  • Privacy, Anonymity, and Security
  • Recommender Systems    
  • Robotics               
  • Web Applications and Internet Data
  • Biologically Plausible Deep Networks

Deep Learning

  • Deep Autoencoders
  • Efficient Inference Methods
  • Generative Models                
  • Interaction-Based Deep Networks
  • Optimization for Deep Networks                
  • Predictive Models
  • Recurrent Networks                
  • Visualization or Exposition Techniques for Deep Networks                

Optimization

  • Combinatorial Optimization
  • Convex Optimization                
  • Non-Convex Optimization
  • Stochastic Optimization

Probabilistic Methods

  • Causal Inference
  • Distributed Inference
  • Gaussian Processes
  • Hierarchical Models
  • MCMC
  • Variational Inference

Reinforcement Learning and Planning

  • Decision and Control
  • Exploration
  • Markov Decision Processes
  • Model-Based RL
  • Multi-Agent RL
  • Navigation
  • Reinforcement Learning

Theory

  • Computational Complexity
  • Control Theory
  • Frequentist Statistics
  • Hardness of Learning and Approximations
  • Learning Theory

A full list of papers by theme below

NeurIPS 2019 analysis of papers by theme

 

 

Image source: Yandex @neurips

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