SRI Blog

-By Jonathan Frankle† MIT CSAIL  David J. Schwab CUNY ITS and Ari S. Morcos  of Facebook AI Research Paper link Many important aspects of neural network learning take place within the very earliest iterations or epochs of training.  For example,  Sparse  Trainable sub-networks emerge  Gradient descent moves into a small subspace  Network undergoes a critical period  Researchers examine the changes that deep neural networks undergo during this early phase of training. Over the past decade, methods for successfully training big, deep neural networks have revolutionized machine learning. Yet surprisingly, the underlying reasons for the success of these approaches remain poorly understood, despite remarkable empirical performance. A large body of work has focused on understanding what happens during the later stages of training, while the initial phase has been less explored.  Research is built on Basic framework "Iterative Magnitude Prun

 Open Problems and Challenges in Ownership Management -By John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Shan He, Ralf Lämmel, Erik Meijer, Silvia Sapora, and Justin Spahr-Summers Facebook Inc.  Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and

- By Rogelio A. Mancisidora, , Michael Kampffmeyer  , Kjersti Aas  , Robert Jenssen  UiT Machine Learning Group Paper Link Abstract Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. Specifically, the Weight of Evidence (WoE) transformation encapsulates the propensity to fall into financial distress and the latent space in the VAE preserves this characteristic in a well-defined clustering structure. These clusters have considerably different risk profiles and therefore are suitable not only for credit scoring but also for marketing and customer purposes. This new clustering methodology offers solutions to some of the challenges in the existing clustering algorithms, e.g., suggests the number of clusters