Deep Learning
Learning Invariants through Soft Unification
Humans learn what variables are and how to use them at a young age.
Addressing Failure Prediction by Learning Model Confidence
Charles Corbiรจre, Nicolas THOME, Avner Bar-Hen, Matthieu Cord, Patrick Pรฉrez
Neural Information Processing Systems http://nips.cc/
Multi-label Contrastive Predictive Coding
V ariational mutual information (MI) estimators are widely used in unsupervised representation learning methods such as contrastive predictive coding (CPC).
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Lixin Fan, Kam Woh Ng, Chee Seng Chan
DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
Neural Information Processing Systems http://nips.cc/
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
We study how their representations differ across layer depth, context length, and attention type.
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex
Jianghong Shi, Eric Shea-Brown, Michael Buice
Neural Information Processing Systems http://nips.cc/