121 citations · 271 across the 9 of their papers we have counts for
5 papers · 1 filter
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Laurent Orseau, Ankit Anand +5
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of dom…
Training a First-Order Theorem Prover from Synthetic Data
Vlad Firoiu, Eser Aygun, Ankit Anand +6
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings
Antoine Bordes, Xavier Glorot, Jason Weston +1
Open-text (or open-domain) semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR). Unfortunately, lar…
Learning invariant features through local space contraction
Salah Rifai, Xavier Muller, Xavier Glorot +3
We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function,…
Adding noise to the input of a model trained with a regularized objective
Salah Rifai, Xavier Glorot, Yoshua Bengio +1
Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relativ…