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
RAGE Against the Machine: Retrieval-Augmented LLM Explanations
Joel Rorseth, Parke Godfrey, Lukasz Golab, Divesh Srivastava, Jaroslaw Szlichta
A Machine Learning-based Approach for Solving Recurrence Relations and its use in Cost Analysis of Logic Programs
Louis Rustenholz, Maximiliano Klemen, Miguel Ángel Carreira-Perpiñán, Pedro López-García
Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging
Nipuni Ginige, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho
Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions
Polina Tsvilodub, Paul Marty, Sonia Ramotowska, Jacopo Romoli, Michael Franke