NeurIPS Conference 2024
2024-12-09 - 2024-12-15 | Vancouver Convention Center, Vancouver, Canada
The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.

ICNC 2024: 3rd International Conference on Neuromorphic Computing
2024-12-13 - 2024-12-15 | Chong Qing, Chong Qing, China
2024 3rd International Conference on Neuromorphic Computing (ICNC 2024) will be held inChong Qing, China on December 13-15, 2024, sponsored by Huazhong University of Science and Technology, co-sponsored by Southwest University.
ICNC 2024 covers many cutting-edge topics such as neural networks, brain-like algorithms, cognitive computing, adaptive learning systems, and the practical applications of these technologies in artificial intelligence, Internet of Things, robotics, medical health and other fields. The conference aims to promote interdisciplinary ideological collisions through various forms such as keynote speeches, symposiums, technical paper reports, and poster displays, and provide participants with a valuable opportunity to communicate directly with leading figures in the field. ICNC 2024 is not only a spark field for the collision of scientific ideas, but also an important bridge to promote the progress and cooperation in the field of neuromorphic computing. We sincerely invite researchers, developers, and people interested in neuromorphic computing from all over the world to jointly create a new era of intelligent computing.

NeuroAI workshop @ NeurIPS 2024
2024-12-14 - 2024-12-15 | Vancouver Convention Center, Vancouver, Canada
Welcome to the NeurIPS 2024 NeuroAI Workshop! This workshop aims to bring together researchers and practitioners from the fields of neuroscience and artificial intelligence.
We are in an era of unprecedented advancement in artificial intelligence, driven by the remarkable progress in artificial neural networks (ANNs) over the past decade. The widespread adoption of these techniques across diverse domains, from language generation (e.g., GPT4o, Claude, Pi, etc) to machine vision (e.g., Sora, DALL-E, etc), highlights the rapid pace of innovation and their transformative impact. This momentum paves the way for exploring the intersections of artificial and natural intelligence – NeuroAI – which promises to unlock novel insights into biological neuronal function and drive the development of computationally less intensive artificial systems trained using small-data regimes.

ML with New Compute Paradigms (MLNCP) at NeurIPS 2024
2024-12-15 | Vancouver Convention Center, Vancouver, Canada
The workshop will take place on Sunday, the 15th December at NeurIPS 2024 in Vancouver, Canada. The currently assigned meeting rooms are 114, 115 in the Vancouver Conference Center.
Digital computing is approaching fundamental limits and faces serious challenges in terms of scalability, performance, and sustainability. At the same time, generative AI is fuelling an explosion in compute demand. There is, thus, a growing need to explore non-traditional computing paradigms, such as (opto-)analog, neuromorphic hardware, and physical systems. Expanding on last year’s successful NeurIPS workshop, which was the first of its kind in this community, we aim to bring together researchers from machine learning and alternative computation fields to establish new synergies between ML models and non-traditional hardware. Co-designing models with specialized hardware, a feature that has also been key to the synergy of digital chips like GPUs and deep learning, has the potential to offer a step change in the efficiency and sustainability of machine learning at scale. Beyond speeding up standard deep learning, new hardware may open the door for efficient inference and training of model classes that have been limited by compute resources, such as energy-based models and deep equilibrium models. So far, however, these hardware technologies have fallen short due to inherent noise, device mismatch, a limited set of compute operations, and reduced bit-depth. As a community, we need to develop new models and algorithms that can embrace and, in fact, exploit these characteristics. This workshop aims to encourage cross-disciplinary collaboration to exploit the opportunities offered by emerging AI accelerators both at training and at inference.
