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Found 5 papers, 0 papers with code
Sparse matrix products for neural network compression
Over-parameterization of neural networks is a well known issue that comes along with their great performance.
A Meta-Learning Approach to One-Step Active Learning
More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot.
LSHTC: A Benchmark for Large-Scale Text Classification
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands).
Representation Learning for cold-start recommendation
Representations for both users and items are computed from the observed ratings and used for prediction.
Learning States Representations in POMDP
We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.
