Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the ...

Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning. A subset of self-improving machine learning in ...

To enhance the robustness of the GTST, we apply split learning (SL) to divide the inference task of disabled UAVs into multiple neural network (NN) blocks that are cooperatively inferred by working ...

A tutorial and reference design for machine learning inference on FPGA-based frame grabber devices in high-throughput imaging applications. This tutorial leverages the hls4ml package and the ...