arXiv:2507. 10005v2 Announce Type: replace Abstract: In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention.
By Yash Arya, Sang Hoon Lee
We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability. Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.
arXiv:2607. 28989v1 Announce Type: new Abstract: Many learning problems require representations that reconcile direct input, nearby structure, and broader context.
By Jose Luis Lima de Jesus Silva
arXiv:2502. 00684v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has successfully addressed many complex control problems.
By Zeyu Jiang, Hai Huang, Xingquan Zuo
arXiv:2607. 07316v1 Announce Type: new Abstract: This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms of modern neural networks.
By Pranav Sawant, Jakub Krej\v{c}\'i
arXiv:2606. 15767v1 Announce Type: cross Abstract: Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains.
By Dong Hyun Jeong, Feng Chen, Jin-Hee Cho, Lance M. Kaplan, Audun J{\o}sang, Soo-Yeon Ji