Technology
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding, guiguang ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu
DNN pruning is an approach for deep model compression, which aimsateliminating someparameters withtolerable performance degradation.
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Neural Information Processing Systems http://nips.cc/
Provable Tempered Overfitting of Minimal Nets and Typical Nets
For both learning rules, we prove overfitting is tempered.
Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov, Elnur Gasanov
Neural Information Processing Systems http://nips.cc/
Nonzero-sum Adversarial Hypothesis Testing Games
Sarath Yasodharan, Patrick Loiseau
Neural Information Processing Systems http://nips.cc/
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization Haitz Sáez de Ocáriz Borde Oxford Robotics Institute University of Oxford Álvaro Arroyo
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure.
DppNet: Approximating Determinantal Point Processes with Deep Networks
Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek
Neural Information Processing Systems http://nips.cc/
f2d887e01a80e813d9080038decbbabb-AuthorFeedback.pdf
Thank you for your detailed reviews and comments. Wehope our clarifications, which we will include in the final1 version of the paper, will strengthen your confidence in the novelty and significance of our results. Both speed up DPP sampling given a polynomial time pre-processing step.
Verifiable Reinforcement Learning via Policy Extraction
Osbert Bastani, Yewen Pu, Armando Solar-Lezama
Trajectoriestakenby , left : s 7! left, and right : s 7! rightareshownas dashededges, rededges, andgreenedges, respectively. Let ={ left : s 7! left, right : s 7! right}, andletg( )= Es d( )[g(s, )]bethe 0-1 loss.