5 papers
Stochastic Gradient Langevin with Delayed Gradients
Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh
Stochastic Gradient Langevin Dynamics (SGLD) ensures strong guarantees with regards to convergence in measure for sampling log-concave posterior distributions by adding noise to st…
Dynamic Graph Operations: A Consistent Non-blocking Approach
Bapi Chatterjee, Sathya Peri, Muktikanta Sa
Graph algorithms enormously contribute to the domains such as blockchains, social networks, biological networks, telecommunication networks, and several others. The ever-increasing…
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent
Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee +2
Machine learning has made tremendous progress in recent years, with models matching or even surpassing humans on a series of specialized tasks. One key element behind the progress…
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee +1
Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsm…
Understanding and Predicting Links in Graphs: A Persistent Homology Perspective
Sumit Bhatia, Bapi Chatterjee, Deepak Nathani +1
Persistent Homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions. For graphical data,…