New Machine
Research on "new machines" broadly encompasses the development and application of machine learning across diverse fields, aiming to improve efficiency, accuracy, and decision-making. Current efforts focus on refining model architectures like convolutional neural networks, gradient boosting machines, and transformers for tasks ranging from image and signal processing to complex prediction and control problems. This research is significant because it drives advancements in various sectors, including healthcare, energy, manufacturing, and transportation, by enabling automated processes, improved diagnostics, and more efficient resource allocation.
Papers
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
Omer Subasi, Oceane Bel, Joseph Manzano, Kevin Barker
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Kushin Mukherjee, Holly Huey, Xuanchen Lu, Yael Vinker, Rio Aguina-Kang, Ariel Shamir, Judith E. Fan
Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines
Rose E. Guingrich, Michael S. A. Graziano
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal
Jiaeli Shi, Najah Ghalyan, Kostis Gourgoulias, John Buford, Sean Moran
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
David Black, Declan Byrne, Anna Walke, Sidong Liu, Antonio Di leva, Sadahiro Kaneko, Walter Stummer, Septimiu Salcudean, Eric Suero Molina
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks
Jack Hutchins, Shamiul Alam, Dana S. Rampini, Bakhrom G. Oripov, Adam N. McCaughan, Ahmedullah Aziz