Industry
Learning to Navigate in Cities Without a Map
Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, koray kavukcuoglu, Andrew Zisserman, Raia Hadsell
Neural Information Processing Systems http://nips.cc/
PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Neural Information Processing Systems http://nips.cc/
Transfusion: UnderstandingTransferLearningfor MedicalImaging
This basic formula has seen almost universal adoption across many different medical specialties.
Point process latent variable models of larval zebrafish behavior
Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman
Each trial consists ofasequence of upto350swimbouts(b.)
Practical Methods for Graph Two-Sample Testing
Debarghya Ghoshdastidar, Ulrike von Luxburg
We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants.
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional objectoriented framework that has provably efficient learning bounds with respect to samplecomplexity.
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models
Full prompts are available in Appendix A.1.