715 citations · 753 across the 7 of their papers we have counts for
6 papers
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
Yunfei Teng, Jing Wang, Anna Choromanska
Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a defined learning rate schedule, i.e., the learning ra…
ESAFE: Enterprise Security and Forensics at Scale
Bernard McShea, Kevin Wright, Denley Lam +7
Securing enterprise networks presents challenges in terms of both their size and distributed structure. Data required to detect and characterize malicious activities may be diffuse…
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
Yacine Jernite, Anna Choromanska, David Sontag
We consider multi-class classification where the predictor has a hierarchical structure that allows for a very large number of labels both at train and test time. The predictive po…
The Loss Surfaces of Multilayer Networks
Anna Choromanska, Mikael Henaff, Michael Mathieu +2
We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-g…
Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
Apoorv Agarwal, Anna Choromanska, Krzysztof Choromanski
In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND an…
Differentially- and non-differentially-private random decision trees
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski +1
We consider supervised learning with random decision trees, where the tree construction is completely random. The method is popularly used and works well in practice despite the si…