139 citations · 164 across the 4 of their papers we have counts for
5 papers
An Empirical Investigation of Beam-Aware Training in Supertagging
Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatc…
Towards modular and programmable architecture search
Renato Negrinho, Darshan Patil, Nghia Le +3
Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-…
Learning Beam Search Policies via Imitation Learning
Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do…
DeepArchitect: Automatically Designing and Training Deep Architectures
Renato Negrinho, Geoff Gordon
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization f…
Efficient Multitask Feature and Relationship Learning
Han Zhao, Otilia Stretcu, Alex Smola +1
We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input…