9.8k citations
- California Institute of TechnologyUS94 papers
- Inter-University Centre for Astronomy and AstrophysicsIN76 papers
- Max Planck Institute for Gravitational PhysicsDE74 papers
- Pennsylvania State UniversityUS71 papers
- University of Maryland, College ParkUS69 papers
- Leibniz University HannoverDE64 papers
- Massachusetts Institute of TechnologyUS64 papers
- Stanford UniversityUS63 papers
- University of MichiganUS63 papers
- University of BirminghamGB62 papers
- University of FloridaUS62 papers
- University of SouthamptonGB62 papers
4 papers · 2 filters
A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
Alireza Ghods, Diane J Cook
Deep neural networks have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve…
Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
Garrett Wilson, Diane J. Cook
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data…
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Chen Sun, Per Karlsson, Jiajun Wu +2
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of…
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Esteban Real +3
Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to r…