Statistical Learning
TopoSRL: Topology Preserving Self-Supervised Simplicial Representation Learning
This paper proposes an SSL method for simplicial complex data that preserves topological and geometric information while learning representations. Although no existing studies focus on SSL for simplicial complex data, a closely related field of SSL for graph data has been extensively studied.
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
This paper deals with the problem of efficient sampling from a stochastic differential equation, given the drift function and the diffusion matrix.
An information-theoretic quantification of the content of communication between brain regions
Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function.
Post-processing Private Synthetic Data for Improving Utility on Selected Measures Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava MIT-IBM Watson AI Lab
The advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information.
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience.