Federated Learning is a machine learning setting where the goal is to train a high-quality
centralized model while training data remains distributed over a large number of clients each …

This document proposes a collection of simplified models relevant to the design of new-physics
searches at the Large Hadron Collider (LHC) and the characterization of their results. …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy protection. …

Distributed stochastic gradient descent is an important subroutine in distributed learning. A
setting of particular interest is when the clients are mobile devices, where two important …

We examine the theoretical motivations for long-lived particle (LLP) signals at the LHC in a
comprehensive survey of standard model (SM) extensions. LLPs are a common prediction of …

We explore the redundancy of parameters in deep neural networks by replacing the
conventional linear projection in fully-connected layers with the circulant projection. The circulant …

We consider the large-scale query-document retrieval problem: given a query (eg, a question),
return the set of relevant documents (eg, paragraphs containing the answer) from a large …

Large scale recommender models find most relevant items from huge catalogs, and they
play a critical role in modern search and recommendation systems. To model the input space …

Federated learning (FL) has recently attracted increasing attention from academia and
industry, with the ultimate goal of achieving collaborative training under privacy and …

Attribute-based representation has shown great promises for visual recognition due to its
intuitive interpretation and cross-category generalization property. However, human efforts are …