Industry
On Making Stochastic Classifiers Deterministic
Andrew Cotter, Maya Gupta, Harikrishna Narasimhan
Neural Information Processing Systems http://nips.cc/
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?
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/
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.
Wasserstein convergence of ˇ Cech persistence diagrams for samplings of submanifolds
A multiset is a set where each element appears with some non-zero multiplicity.
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
In disciplines such as chemistry, physics, and network science, graphs are a convenient and well-studied mathematical object to represent such interacting entities and their attributes.
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI T obit Klug 1, Kun Wang 1, Stefan Ruschke
In this paper, we propose MotionTTT a deep learning-based test-time-training (TTT) method for accurate motion estimation.