Argmax
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About
Argmax serves as a comprehensive academic repository and publication index for the research conducted by Patrick van der Smagt and his collaborators. The platform acts as a centralized knowledge base, tracking the evolution of artificial intelligence, robotics, and machine learning from the early 1990s through the present day. It provides a structured overview of technical contributions to fields as varied as deep reinforcement learning, world models, and AI regulation, making it a valuable resource for academics and industry professionals seeking foundational research. By organizing 185 distinct publications, the site offers a rare longitudinal view of how neural network architectures and robotic control systems have matured over three decades. The primary function of the site is to catalog scholarly work, including conference papers, journal articles, and preprints. Each entry is indexed by year and includes a direct link to Google Scholar, allowing users to quickly locate citations, abstracts, and full-text versions. Key thematic areas covered include robot eye-hand coordination, variational inference, and the integration of neural networks with physical systems like musculoskeletal robots and pneumatic arms. The repository is updated regularly, ensuring that latest developments—such as research on the EU AI Act or language-informed world models—are accessible to the scientific community for review and implementation. This resource is best suited for research scientists, PhD students, and R&D engineers who require a deep dive into specific robotic control theories or the history of neural network applications. It is particularly useful for those investigating complex topics like latent action spaces, autonomous service robotics, or the intersection of machine learning and neurophysiology. Academic libraries and institutional researchers can also leverage the repository to find primary sources for literature reviews or to track specific methodologies in bionics and tactile sensor calibration. Unlike marketing-heavy directories, Argmax offers a technical perspective, prioritizing peer-reviewed evidence over commercial features. What distinguishes Argmax from generic research databases is its curated focus on the intersection of deep learning and physical robotics. While larger databases are exhaustive, Argmax provides a focused roadmap of a specific research lineage, allowing users to trace the development of concepts such as Deep Variational Bayes Filters or Cerebellar Control across multiple iterations. The site simplifies discovery for researchers interested in the Patrick van der Smagt school of thought, providing a high-signal environment for exploring the technical hurdles and breakthroughs in AI-driven robotics. It remains an essential reference for those bridging theoretical machine learning and practical robotic execution.
Pros & Cons
Provides a comprehensive historical record of AI and robotics research since 1990.
Covers highly specialized topics including neurophysiology and AI regulation.
Direct links to Google Scholar facilitate quick discovery of peer-reviewed content.
Website is a static list of publications rather than an interactive software tool.
Does not host full-text PDFs directly on the landing page.
Use Cases
AI researchers can study the evolution of world models and robotic control to inform their current academic projects.
Machine learning engineers can reference specific technical papers for implementing advanced algorithms in industrial robotics.
Platform
Task
Features
• google scholar integration
• preprint and journal article tracking
• multi-disciplinary coverage (robotics, astrophysics, ethics)
• chronological research indexing
• archive of 185+ publications
Job Opportunities
There are currently no job postings for this AI tool.
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