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2024-04-19 15:00:00
2024-04-19 16:00:00
America/New_York
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms
Abstract: Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data. Among various ANNS algorithms, graph-based algorithms are known to achieve the best throughput-recall tradeoffs. Despite the large scale of modern ANNS datasets, existing parallel graph based implementations suffer from significant challenges to scale to large datasets due to heavy use of locks and other sequential bottlenecks, which 1) prevents them from efficiently scaling to a large number of processors, and 2) results in nondeterminism that is undesirable in certain applications.In this paper, we introduce ParlayANN, a library of deterministic and parallel graph-based approximate nearest neighbor search algorithms, along with a set of useful tools for developing such algorithms. In this library, we develop novel parallel implementations for four state-of-the-art graph-based ANNS algorithms that scale to billion-scale datasets. Our algorithms are deterministic and achieve high scalability across a diverse set of challenging datasets. In addition to the new algorithmic ideas, we also conduct a detailed experimental study of our new algorithms as well as two existing non-graph approaches. Our experimental results both validate the effectiveness of our new techniques, and lead to a comprehensive comparison among ANNS algorithms on large scale datasets with a list of interesting findings. This work is joint with Zheqi Shen, Guy Blelloch, Laxman Dhulipala, Yan Gu, Harsha Vardhan Simhadri, and Yihan Sun and appeared in PPoPP 2024.Bio: Magdalen Dobson Manohar is a 5th year PhD student in the Computer Science Department at Carnegie Mellon University advised by Guy Blelloch. She is interested in designing parallel and concurrent algorithms for solving problems related to similarity search, information retrieval, and computing nearest neighbors, with a particular focus on similarity search in high dimensions. She will be joining Microsoft as a Senior Researcher in Summer 2024. She completed her undergraduate degree in mathematics at MIT in Spring 2019.
32-G575
Events
April 19, 2024
April 23, 2024
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2024-04-23 12:00:00
2024-04-23 13:00:00
America/New_York
Visual Computing Seminar | Tim Brooks - Sora: Video Generation Models as World Simulators
Virtual session of MIT Visual Computing Seminar, Spring 2024 featuring invited speaker (remote) Tim Brooks from OpenAI.The format is ~25 min of talk followed by Q&A. Considering the potential capacity of the talk, we use slido for live Q&A and answer top questions from the upvote queue. [live Q&A link] https://tinyurl.com/TimBrooksMITPlease DO NOT record this talk by any means. Thanks for your understanding. TitleSora: Video Generation Models as World SimulatorsAbstractWe explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.Bio Tim Brooks is a research scientist at OpenAI where he co-leads Sora, their video generation model. His research investigates large-scale generative models that simulate the physical world. Tim received a PhD at Berkeley AI Research advised by Alyosha Efros, where he invented InstructPix2Pix. He previously worked on AI that powers the Pixel phone's camera at Google and on video generation models at NVIDIA.
https://mit.zoom.us/j/95167636032?pwd=U0dyaEx1a3A3QkZrbmIvMkcvUFkyUT09 (password: mitvc)
June 07, 2024
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2024-06-07 9:00:00
2024-06-07 18:00:00
America/New_York
CSAIL + Imagination in Action Symposium 2024
The symposium will showcase the extraordinary and substantive contributions CSAIL research groups have made, and highlight the remarkable impacts of our work.
Kirsch Auditorium