Deep Learning
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers Guang-He Lee 1, Y ang Yuan
Many powerful classifiers lack robustness in the sense that a slight, potentially unnoticeable manipulation of the input features, e.g., by an adversary, can cause the classifier to change its prediction [
Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
Neural Information Processing Systems http://nips.cc/
AI Isn't Coming for Hollywood. It Has Already Arrived
Lady Gaga probably wasn't thinking that a coup would unfold in her greenhouse. Then again, she was cohosting a party there with Sean Parker, the billionaire founder of Napster and first president of Facebook. It was February 2024, and the singer had invited guests to her 22.5 million oceanside estate in Malibu to mark the launch of a skin-care nonprofit. One of the organization's trustees was her boyfriend, whose day job was running the Parker Foundation. In the candlelit space, beside floor-to-ceiling windows that looked out over the Pacific, Parker's people mingled with Gaga's, nibbling focaccia and branzino alla brace to music from a string quartet (Grammy-winning, of course).
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
W. O. K. Asiri Suranga Wijesinghe, Qing Wang
Neural Information Processing Systems http://nips.cc/
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem, John P. Cunningham
Here we show that, far from being a triviality or nuisance that is convenient to ignore, this error has profound importance to V AE, both qualitative and quantitative.
MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration
Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi
Deep neural networks achieve outstanding results for challenging image classification tasks.
Certifying Geometric Robustness of Neural Networks
To address this issue, one would ideally like to prove that a given network is free of such geometric adversarial examples.
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee
Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time.