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Densely Connected Attention Propagation for Reading Comprehension
Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su
We conduct extensive experiments on four challenging RC benchmarks.
Learning and Testing Causal Models with Interventions
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
Bayesian network on a given graph.
Learning to Play With Intrinsically-Motivated, Self-Aware Agents
Nick Haber, Damian Mrowca, Stephanie Wang, Li F. Fei-Fei, Daniel L. Yamins
Neural Information Processing Systems http://nips.cc/
A probabilistic population code based on neural samples
Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj, Ralf Haefner
As a result, the brain needs to perform these computations in an approximate manner.
Reducing Network Agnostophobia
Akshay Raj Dhamija, Manuel Gรผnther, Terrance Boult
Neural Information Processing Systems http://nips.cc/
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations.
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
Neural Information Processing Systems http://nips.cc/
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, Nando de Freitas
Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey Spence, Paul Jenkins, Sara Mathieson, Yun Song
An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data.
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Sampath Kannan, Jamie H. Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
We give a smoothed analysis, showing that even when contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve "no regret", perhaps (depending on the specifics of the setting) with a constant amount of initial training data.