Search Results for author:
Found 6 papers, 4 papers with code
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach
We applied and tested our method in particular to some benchmark instances of Job Shop Problem, but this technique is general enough to be potentially used to tackle other different optimal job scheduling tasks with minimal intervention.
Regularization-based Pruning of Irrelevant Weights in Deep Neural Architectures
Deep neural networks exploiting millions of parameters are nowadays the norm in deep learning applications.
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation
Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many tasks.
Controlling Hallucinations at Word Level in Data-to-Text Generation
Specifically, we propose a Multi-Branch Decoder which is able to leverage word-level labels to learn the relevant parts of each training instance.
Ranked #3 on
Table-to-Text Generation
on WikiBio
Copy mechanism and tailored training for character-based data-to-text generation
In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation.
Ranked #2 on
Data-to-Text Generation
on E2E NLG Challenge
OCReP: An Optimally Conditioned Regularization for Pseudoinversion Based Neural Training
In this paper we consider the training of single hidden layer neural networks by pseudoinversion, which, in spite of its popularity, is sometimes affected by numerical instability issues.
