A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

Patronus AI unveiled “Generative Simulators,” adaptive “practice worlds” that replace static benchmarks with dynamic reinforcement-learning environments to train more reliable AI agents for complex, ...

In an interview, Brad Ingelsby, who created this HBO crime drama, discusses the series finale and whether anyone in the Delco region ever has a nice day. In an interview, Brad Ingelsby, who created ...

According to @AIatMeta, DINOv3 leverages self-supervised learning (SSL) to train on 1.7 billion images using a 7-billion-parameter model without the need for labeled data, which is especially ...

Abstract: Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication can ...

Abstract: Object pose estimation using learning-based methods often necessitates vast amounts of meticulously labeled training data. The process of capturing real-world object images under diverse ...

Overall framework of STiL. STiL encodes image-tabular data using $\phi$, decomposes modality-shared and -specific information through DCC $\psi$ (a), and outputs ...