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| NeurIPS Workshops | |
|---|---|
| Name | NeurIPS Workshops |
| Discipline | Machine learning; Artificial intelligence |
| Frequency | Annual |
| Established | 1987 |
| Venue | Rotating (often Vancouver; previously Denver, Long Beach, Montréal, New Orleans, Barcelona, Montreal) |
| Organizer | Neural Information Processing Systems Foundation; NeurIPS |
NeurIPS Workshops
NeurIPS Workshops are a series of satellite meetings held alongside the annual Neural Information Processing Systems conference. They provide forums for focused discussion, rapid dissemination, and community building among researchers from institutions such as Google Research, DeepMind, OpenAI, Microsoft Research, Facebook AI Research and laboratories at Stanford University, Massachusetts Institute of Technology, University of California, Berkeley, Carnegie Mellon University.
Workshops run concurrently with the main conference and typically address specialized topics in machine learning and artificial intelligence, attracting attendees from organizations including IBM Research, Amazon Web Services, Tesla, NVIDIA, Intel Labs. They feature formats like panels, presentations, tutorials, and poster sessions, often involving speakers from universities such as Harvard University, Princeton University, University of Toronto, ETH Zurich and institutes like Allen Institute for AI, Max Planck Society, CNRS.
Workshops emerged alongside the conference founded in 1987, evolving through eras marked by milestones tied to figures and events like the rise of deep learning popularized by teams at University of Toronto with researchers affiliated with Geoffrey Hinton, work tied to Yann LeCun at New York University, and breakthroughs from Ian Goodfellow and others at OpenAI. Venues and program policies changed during relocations to cities such as Montréal and Vancouver and were influenced by institutional shifts at California Institute of Technology, University of Oxford, University of Cambridge, and conferences like ICML, CVPR, AAAI.
Workshop proposals are evaluated by program chairs drawn from committees with members from Neural Information Processing Systems Foundation, Google Research, DeepMind, Microsoft Research. Submission components mirror practices used by venues like ICLR and ACL, requiring organizers to specify themes, chairs, and potential keynote speakers from institutions such as Yale University, Columbia University, University of Washington, Peking University. Acceptances are announced alongside conference schedules that coordinate with venues such as Vancouver Convention Centre and logistics partners including Eventbrite-style services and travel facilitation by corporate sponsors like NVIDIA and Intel.
Workshop topics span technical and applied themes evident in papers and talks by researchers from Berkeley AI Research (BAIR), Google Brain, Facebook AI Research, DeepMind and groups at University College London and Tsinghua University. Frequent themes include deep learning methods tied to breakthroughs by researchers at Stanford University and University of Toronto, reinforcement learning work related to David Silver and Demis Hassabis-affiliated teams at DeepMind, and fairness, interpretability, and ethics discussions involving scholars from Oxford Internet Institute, Harvard Kennedy School, MIT Media Lab, and non-profits like OpenAI and Partnership on AI.
Some workshops have catalyzed directions later reflected in mainstream venues and industry roadmaps from Google DeepMind, OpenAI, NVIDIA Research, and Microsoft Research AI. Topics incubated at workshops have influenced deployments at companies such as Amazon, Apple Inc., Uber, Waymo and collaborations with government labs like Los Alamos National Laboratory and Sandia National Laboratories. Workshop outputs have informed standards and tools from entities like TensorFlow-affiliated teams, PyTorch developers at Meta Platforms, Inc., and academic curricula at Carnegie Mellon University and University of California, Berkeley.
Participants range from students affiliated with programs at Princeton University, Cornell University, University of Illinois Urbana-Champaign, to senior researchers at Google Research, DeepMind, OpenAI, Facebook AI Research. Workshops foster networks connecting startups incubated at accelerators like Y Combinator and labs housed in innovation districts near institutions such as Stanford University and MIT. Community organizers often include members from societies like IEEE and partnerships with publishers like ACM.
Workshops have faced critique over commercialization tied to sponsors such as Google, Microsoft, NVIDIA and perceived gatekeeping linked to program committees populated by affiliates of DeepMind, OpenAI, Facebook AI Research. Debates echo controversies from venues like ICML and CVPR about reproducibility, peer review standards, and conflicts of interest involving academics from Stanford University, University of California, Berkeley, Massachusetts Institute of Technology. Security and ethical concerns discussed at workshops intersect with policy debates in institutions like European Commission, US National Science Foundation, UNESCO and advocacy groups such as Electronic Frontier Foundation.
Category:Machine learning conferences