Industry
Targeting EEG/LFP Synchrony with Neural Nets
Yitong Li, michael Murias, samantha Major, geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson
We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are "big" in terms of the size of recorded data but
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya
We introduce Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced model selection and estimation.
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing
In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component.
Sobolev Training for Neural Networks
Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, Razvan Pascanu
At the heart of deep learning we aim to use neural networks as function approxi-mators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process.
Hierarchical Attentive Recurrent Tracking
Adam Kosiorek, Alex Bewley, Ingmar Posner
Neural Information Processing Systems http://nips.cc/
VAIN: Attentional Multi-agent Predictive Modeling
One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce V AIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that V AIN is effective for multi-agent predictive modeling.
A Screening Rule for l1-Regularized Ising Model Estimation
Zhaobin Kuang, Sinong Geng, David Page
While the field of statistical learning with sparsity [Hastie et al., 2015] has been steadily rising to
Predicting Scene Parsing and Motion Dynamics in the Future
Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin, Yunpeng Chen, Zequn Jie, Jiashi Feng, Shuicheng Yan
The ability of predicting the future is important for intelligent systems, e.g.
On Frank-Wolfe and Equilibrium Computation
Jacob D. Abernethy, Jun-Kun Wang
Neural Information Processing Systems http://nips.cc/
This Hacker Conference Installed a Literal Anti-Virus Monitoring System
At New Zealand's Kawaiican cybersecurity convention, organizers hacked together a way for attendees to track CO levels throughout the venue--even before they arrived. Hacker conferences--like all conventions--are notorious for giving attendees a parting gift of mystery illness. To combat "con crud," New Zealand's premier hacker conference, Kawaiicon, quietly launched a real-time, room-by-room carbon dioxide monitoring system for attendees. To get the system up and running, event organizers installed DIY CO monitors throughout the Michael Fowler Centre venue before conference doors opened on November 6. Attendees were able to check a public online dashboard for clean air readings for session rooms, kids' areas, the front desk, and more, all before even showing up. It's ALMOST like we are all nerds in a risk-based industry, the organizers wrote on the convention's website.