Statistical Learning
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
The purpose of this work is to study the general properties of this quantity and to show how they can be leveraged in practice.
COLA: Decentralized Linear Learning
Neural Information Processing Systems http://nips.cc/
Generalization Bounds for Uniformly Stable Algorithms
Neural Information Processing Systems http://nips.cc/
Objective and efficient inference for couplings in neuronal networks
Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima
Inferring directional couplings from the spike data of networks is desired in various scientific fields such as neuroscience. Here, we apply a recently proposed objective procedure to the spike data obtained from the Hodgkin-Huxley type models and in vitro neuronal networks cultured in a circular structure. As a result, we succeed in reconstructing synaptic connections accurately from the evoked activity as well as the spontaneous one. To obtain the results, we invent an analytic formula approximately implementing a method of screening relevant couplings. This significantly reduces the computational cost of the screening method employed in the proposed objective procedure, making it possible to treat large-size systems as in this study.
A Spectral View of Adversarially Robust Features
Shivam Garg, Vatsal Sharan, Brian Zhang, Gregory Valiant
Neural Information Processing Systems http://nips.cc/
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Neural Information Processing Systems http://nips.cc/
KDGAN: Knowledge Distillation with Generative Adversarial Networks
Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi
Neural Information Processing Systems http://nips.cc/
On GANs and GMMs
Natural images take up only a tiny fraction of the space of possible images.
Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification Xi Y ang
Although numerous studies have been emerged on adversarial attacks and defenses in fields such as face recognition, person re-identification, and pedestrian detection, there is currently a lack of research on the security of VIReID systems.
The Power of Extrapolation in Federated Learning
In our work, we focus on smooth convex or strongly convex problems in the interpolation regime.