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2024-04-23 12:00:00
2024-04-23 13:00:00
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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)
Events
April 23, 2024
April 24, 2024
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2024-04-24 16:00:00
2024-04-24 17:00:00
America/New_York
Lipschitz Continuous Graph Algorithms via Proximal Gradient Analysis
Abstract: We study the class of Lipschitz continuous graph algorithms as introduced in the recent work of Kumabe and Yoshida [FOCS'23]. The Lipschitz constant of an algorithm, intuitively, bounds the ratio of the changes in its output over the perturbations of its input. Our approach consists of three main steps. First, we consider a natural convex relaxation of the underlying graph problem with the addition of a smooth and strongly convex regularizer. Then, we give upper bounds on the ell-1 distance between the optimal solutions of the convex programs, under small perturbations of the weights, via a stability analysis of the trajectory of the proximal gradient method. Finally, we present new problem-specific rounding techniques to obtain integral solutions to several graph problems that approximately maintain the stability guarantees of the fractional solutions. We apply our framework to a number of problems including minimum s-t cut, multiway cut, densest subgraph, maximum b-matching, and packing integer programs. Finally, we show the tightness of our results for certain problems by establishing matching lower bounds.
32-G575
April 25, 2024
Theory and Practice of Fair Food Allocation
Purdue University
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2024-04-25 16:00:00
2024-04-25 17:00:00
America/New_York
Theory and Practice of Fair Food Allocation
Abstract: Food rescue organizations worldwide are leading programs aimed at addressing food waste and food insecurity. Food Drop is such a program, run by the non-governmental organization Indy Hunger Network (IHN), in the state of Indiana, in the United States. Food Drop matches truck drivers with rejected truckloads of food --- food that would otherwise be discarded at a landfill --- to food banks. Matching decisions are currently made by the Food Assistance Programs Manager of IHN. In this talk, I will discuss a partnership with IHN with the goal of completely automating Food Drop. Motivated by this collaboration, I will present a series of theoretical models and results for the fair division of indivisible goods, with each model refining our understanding of the essence of IHN's problem. These results directly informed our choice of algorithm for matching drivers to food banks for the platform we built for IHN.
32-D507
April 29, 2024
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2024-04-29 13:00:00
2024-04-29 14:00:00
America/New_York
ML-enabled Genetic Analysis of High-Content Phenotypes
Abstract: In my talk, I will discuss new machine learning (ML) approaches for human genetics. First, I will present ML-enhanced genetic analysis of histological traits, where we leverage a novel semantic autoencoder to compress histological images into trait embeddings for GWAS. In an application to multiple tissues from the GTEx dataset, we discover 4 genome-wide significant loci associated with histological changes, which we can visualise and interpret for each of the discovered variants thanks to our decoder.Second, I will introduce a new method combining machine learning and genetic causal inference for risk predictions. A key advantage of this method is that it doesn't require longitudinal data. This allows for risk prediction of late-onset diseases in large biobanks, where follow-up cases are often limited.Overall, these contributions demonstrate the transformative power of ML in human genetics. Our approaches enable more nuanced analyses of high-dimensional traits and facilitate biomarker discovery.Bio: Francesco Paolo Casale studied physics at the University of Naples Federico II, Italy. He received his PhD in statistical genetics at the University of Cambridge and the European Bioinformatics Institute in 2016, where he developed new computational methods for genetic association studies and contributed to landmark international projects such as the last phase of the 1000 Genomes Project and the Blueprint initiative. He conducted his postdoctoral studies at the Microsoft Research New England lab in Boston, working on deep generative models for imaging genetics and automated machine learning. In 2019, he joined insitro, a drug discovery and development company located in the bay area. There, he led the statistical genetics team, working at the intersection of human genetics, machine learning and functional genomics to enable target identification and characterization. Since January 2022, he is a Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute of AI for Health.
D507