Home /
Research
Showing 187 - 192 / 904
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
MingzhiDong, XiaochenYang, RuiZhu....
Published date-12/01/2020
MetricLearning
Metric learning aims to learn a distance measure that can benefit distance-based methods such as the nearest neighbour (NN) classifier. While considerable efforts have been made to improve its empirical …
Lipschitz-Certifiable Training with a Tight Outer Bound
SungyoonLee, JaewookLee, SaeromPark....
Published date-12/01/2020
Verifiable training is a promising research direction for training a robust network. However, most verifiable training methods are slow or lack scalability. In this study, we propose a fast and …
Towards Neural Programming Interfaces
ZacharyBrown, NathanielRobinson, DavidWingate....
Published date-12/01/2020
LanguageModelling, TextGeneration
It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of …
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
MichaelDennis, NatashaJaques, EugeneVinitsky....
Published date-12/01/2020
TransferLearning
A wide range of reinforcement learning (RL) problems --- including robustness, transfer learning, unsupervised RL, and emergent complexity --- require specifying a distribution of tasks or environments in which a …
Neuronal Gaussian Process Regression
JohannesFriedrich....
Published date-12/01/2020
The brain takes uncertainty intrinsic to our world into account. For example, associating spatial locations with rewards requires to predict not only expected reward at new spatial locations but also …
PMLB v1.0: an open source dataset collection for benchmarking machine learning methods
TrangT.Le, WilliamLaCava, JosephD.Romano....
Published date-11/30/2020
Multi-classClassification
PMLB (Penn Machine Learning Benchmark) is an open-source data repository containing a curated collection of datasets for evaluating and comparing machine learning (ML) algorithms. Compiled from a broad range of …