Technology
Recurrent Relational Networks
Rasmus Palm, Ulrich Paquet, Ole Winther
As an illustrative example, consider solving a Sudoku.
Learning To Learn Around A Common Mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Neural Information Processing Systems http://nips.cc/
Cross-Modal Learning with Adversarial Samples
CHAO LI, Shangqian Gao, Cheng Deng, De Xie, Wei Liu
A novel cross-modal adversarial sample learning algorithm is presented.
A Properties of the Transfer operator
In this section, we outline the'relaxation' or'decay' of the spectral components of Each trajectory is simulated for 100000 steps. A separate testing set was generated in an identical manner but with a different random seed. The original data was obtained upon request from DE Shaw Research, and details about the simulations are available in the original publication [19]. Figures 8, 9 and 10, show conditional distributions generated by CG-SE3-ITO models and comparisons of MD with ITO simulations on the fast folders Trp-Cage, BBA, and Villin, respectively. Reference value and observables We compute observables using Markov state models.
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities.
Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir Kim, Bryan Russell, Mathieu Aubry
Weproposetorepresent shapesasthedeformation andcombination oflearnable elementary 3D structures, which are primitives resulting from training over a collection of shapes.
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang
Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors. Recent research has demonstrated the widespread presence and the devastating consequences of such attacks.