An anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
The color-correcting tool, known as “SeaSplat,” reveals more realistic colors of underwater features.
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
With a novel simulation method, robots can guess the weight, softness, and other physical properties of an object just by picking it up.
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
New type of “state-space model” leverages principles of harmonic oscillators.
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.
A new technique automatically guides an LLM toward outputs that adhere to the rules of whatever programming language or other format is being used.
By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
A new method from the MIT-IBM Watson AI Lab helps large language models to steer their own responses toward safer, more ethical, value-aligned outputs.
The approach maintains an AI model’s accuracy while ensuring attackers can’t extract secret information.
This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.