In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Where Medical Statistics Meets Artificial Intelligence | New England Journal of Medicine Skip to
main content The New England Journal of Medicine homepage Advanced Search SEARCH …

In this paper, we make the key delineation on the roles of resolution and statistical
uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

Integrated photonic neural networks provide a promising platform for energy-efficient, high-
throughput machine learning with extensive scientific and commercial applications. Photonic …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

The significant advances in applications of text-to-image generation models have prompted
the demand of a post-hoc adaptation algorithms that can efficiently remove unwanted …

The last 3 years have resulted in machine learning (ML)-based image generators with the
ability to output consistently higher quality images based on natural language prompts as …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …