Deep Learning
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a DRL policy into behaving sub optimally.
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie
Compressing neural networks by pruning weights with small magnitudes can significantly reduce the computation and storage cost. Although pruning makes the model smaller, it is difficult to get a practical speedup in modern computing platforms such as CPU and GPU due to the irregularity.
Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Bayesian Distributed Stochastic Gradient Descent
Neural Information Processing Systems http://nips.cc/
Evolution-Guided Policy Gradient in Reinforcement Learning
Neural Information Processing Systems http://nips.cc/
Trajectory Convolution for Action Recognition
Yue Zhao, Yuanjun Xiong, Dahua Lin
How to leverage the temporal dimension is one major question in video analysis.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein
Work done as a member of the Google AI Residency program (g.co/airesidency).
Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
We prove the existence of adversarial perturbations that transfer across different classifiers.
End-to-End Differentiable Physics for Learning and Control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter
We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.
SLAYER: Spike Layer Error Reassignment in Time
Sumit Bam Shrestha, Garrick Orchard
Artificial Neural Networks (ANNs), especially Deep Neural Networks, have become the go-to tool for many machine learning tasks.