Industry
Addressing Failure Prediction by Learning Model Confidence
Charles Corbière, Nicolas THOME, Avner Bar-Hen, Matthieu Cord, Patrick Pérez
Neural Information Processing Systems http://nips.cc/
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Lixin Fan, Kam Woh Ng, Chee Seng Chan
Extensiveexperimental results justify the effectiveness oftheproposed passport-based DNNownership verification schemes.
This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
Bayesian Optimization (BO) is an effective framework for black-box optimization.
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)
Weusebrainimagingrecordings ofsubjectsreading complex natural text to interpret word and sequence embeddings from4 recent NLP models - ELMo, USE, BERT and Transformer-XL. We study how their representations differ across layer depth, contextlength, 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/