Machine learning methodologies encompass algorithmic frameworks and architectures for training, optimizing, and deploying models such as neural networks, transformers, diffusion models, and reinforcement learning agents. Work in this area develops new training objectives, curriculum schemes, speculative and efficient decoding, pruning and communication-reduction strategies, and biologically inspired or physics-informed architectures. The domain also includes safety preservation, unlearning, scaling laws, and specialized methods for vision, language, control, and scientific computing, aiming to improve performance, efficiency, robustness, and controllability of complex AI systems.
Energy & Green Tech
A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output adjustments, according to a study led by University of Michigan researchers, published in the journal ...
Jun 30, 2025
0
30
Energy & Green Tech
Time-consuming testing and computer simulations are bottlenecks in the design of new materials. A thesis from the University of Gothenburg aims to develop an AI model that can efficiently determine the durability and strength ...
Jun 24, 2025
0
33
Hardware
BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the Institute of Science Tokyo, ...
Jun 23, 2025
0
48
Computer Sciences
Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from natural language descriptions. However, when a typical example like the Stable Diffusion model is given ...
Jun 20, 2025
1
98
Computer Sciences
Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI-like chatbots so good at understanding and using words. The work is published in the journal Physical ...
Jun 20, 2025
0
48
Machine learning & AI
Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition ...
Robotics
Recent advances in the field of robotics have enabled the automation of various real-world tasks, ranging from the manufacturing or packaging of goods in many industry settings to the precise execution of minimally invasive ...
Hardware
The promise of a new type of computer chip that could reshape the future of artificial intelligence and be more environmentally friendly is explored in a technology review paper published by UC Riverside engineers in the ...
Jun 17, 2025
0
93
Robotics
A new, densely annotated 3D-text dataset called 3D-GRAND can help train embodied AI, like household robots, to connect language to 3D spaces. The study, led by University of Michigan researchers, was presented at the Computer ...
Jun 16, 2025
0
73
Computer Sciences
A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a decision was made is ...
Jun 13, 2025
0
100
