Deep Learning
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
Neural Information Processing Systems http://nips.cc/
Neural Arithmetic Logic Units
Andrew Trask, Felix Hill, Scott E. Reed, Jack Rae, Chris Dyer, Phil Blunsom
Specifically,one frequently observes failures when quantities that lie outside the numerical range used during training are encountered at test time, even when the target functionissimple (e.g., itdepends only onaggregating counts orlinear extrapolation). This failure patternindicates that the learned behavior is better characterized by memorization than by systematic abstraction.
Deep Structured Prediction for Facial Landmark Detection
Neural Information Processing Systems http://nips.cc/
Harnessing small projectors and multiple views for efficient vision pretraining
We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.
Discriminative Topic Modeling with Logistic LDA
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
Neural Information Processing Systems http://nips.cc/
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
However, visual representations are inherently independent of language.