Statistical Learning
Beyond Grids: Learning Graph Representations for Visual Recognition
For all tasks, our method outperforms state-of-the-art methods.
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
John T. Halloran, David M. Rocke
The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample.
The Physical Systems Behind Optimization Algorithms
Lin Yang, Raman Arora, Vladimir braverman, Tuo Zhao
In particular, we study gradient descent, proximal gradient descent, coordinate gradient descent, proximal coordinate gradient, and Newton's methods as well as their Nesterov's accelerated variants in a unified framework motivated by a
MetaGAN: An Adversarial Approach to Few-Shot Learning
Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
We argue that this GAN-based approach can help few-shot classifiers to learn sharper decision boundary, which could generalize better.
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen
The first two authors contributed equally to this work.
Explaining Deep Learning Models -- A Bayesian Non-parametric Approach
Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation.
Temporal Regularization for Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup
Neural Information Processing Systems http://nips.cc/
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S. Cohen
We present a convolutional network that is equivariant to rigid body motions.