A new analysis reveals that tapping into the full potential of Vision Transformers-not just their final outputs-dramatically improves our ability to identify images created by artificial intelligence.
A new system dramatically accelerates the reinforcement learning process used to fine-tune large language models by optimizing how training data is used.
A new study examines the reliability of current face forgery detection methods when confronted with diverse and unpredictable real-world conditions.
Researchers have developed a novel reinforcement learning framework that stabilizes diffusion models and aligns them better with human expectations.
Researchers have developed a self-supervised learning technique that allows robots and machines to accurately estimate the depth of transparent objects like glass or plastic, enhancing their ability to interact with the world.
A new review explores the crucial interplay between activation functions, data distribution, and adversarial robustness in both centralized and federated machine learning.
Researchers have developed a novel approach to automatically identify and assemble reusable code modules from existing neural network repositories, accelerating development and fostering architectural innovation.
A novel algorithm, MechDetect, helps data scientists understand how errors arise in tabular datasets, leading to more effective data cleaning and reliable machine learning models.
New research reveals that reliable concept signals within transformer models aren’t evenly distributed, but concentrated in a surprisingly small number of highly activated tokens.
A new poker AI, Patrick, prioritizes exploiting human tendencies over achieving game-theoretic perfection, yielding profitable results in real-money play.