Asia
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine
Then, at policy fork steps.
basedexplanationsimproveA I
Our explanations help users more accurately reject AI'swrong decisions than all other tested methods.
Universality and individuality in neural dynamics across large populations of recurrent networks
Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, David Sussillo
Or alternatively, are the learned dynamics highly sensitiveto different model choices?
PALMER: Perception-ActionLoopwithMemory forLong-HorizonPlanning
This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.
Sample Efficient Active Learning of Causal Trees
Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler
Neural Information Processing Systems http://nips.cc/
Structure-Aware Convolutional Neural Networks
Jianlong Chang, Jie Gu, Lingfeng Wang, GAOFENG MENG, SHIMING XIANG, Chunhong Pan
Neural Information Processing Systems http://nips.cc/
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
In this paper, we consider the existence and construction of both log-concave CNDs and multivariate CNDs.