Challenges in representation learning: A report on three machine learning contests (2013-07-01T00:00:00.000000Z)

TL;DR

The datasets created for these challenges are described, the results of the competitions are summarized, and some comments are provided on what kind of knowledge can be gained from machine learning competitions.

Authors

Radu Tudor Ionescu

13 papers

Yoshua Bengio

69 papers

Aaron C. Courville

35 papers

Challenges in representation learning: A report on three machine learning contests

TL;DR

Authors

References25 items

Objectness to improve the bag of visual words model

Pylearn2: a machine learning research library

Constructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative Choice

Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?

Deep Learning using Linear Support Vector Machines

Maxout Networks

Large-Scale Feature Learning With Spike-and-Slab Sparse Coding

Representation Learning: A Review and New Perspectives

Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

Sparse Filtering

Unsupervised and transfer learning challenge

Extracting and composing robust features with denoising autoencoders

Training a Support Vector Machine in the Primal

Semi-supervised Learning by Entropy Minimization

Labeling images with a computer game

Object recognition from local scale-invariant features

Support-Vector Networks

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition

A Deep Learning Approach with an Ensemble-Based Neural Network Classifier for Black Box ICML 2013 Contest

Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

Reading Digits in Natural Images with Unsupervised Feature Learning

NIPS Workshop on Challenges in Learning Hierarchical Models: Transfer Learning and Optimization

Random Forests

Hinton . The Toronto face dataset

Field of Study

Journal Information

Name

Page

Volume

Venue Information

Name

Type

URL

Alternate Names