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C3 Digital Transformation Institute
Добавлен 29 апр 2020
The C3.ai Digital Transformation Institute is a research consortium dedicated to accelerating the benefits of artificial intelligence for business, government, and society.
Re-Evaluating Open Source vs. Proprietary LLMs: Overlooked Challenges and Missed Opportunities
SPEAKER: Hao Peng is an assistant professor at the Department of Computer Science of the University of Illinois Urbana-Champaign. His research interest spans natural language processing and machine learning. His current interests primarily include making language AI more efficient and accessible, and evaluating and improving large-scale language models’ reasoning capabilities, factuality, and trustworthiness.
Просмотров: 164
Видео
Sampling Diffusion Models in the Era of Generative AI
Просмотров 357Год назад
SPEAKER: Morteza Mardani is a Senior Research Scientist at NVIDIA focusing on generative learning. Concurrently, he is a visiting researcher within the Electrical Engineering department at Stanford University. Morteza’s journey includes previous appointments as a postdoctoral researcher and research associate at Stanford, and visiting scholar at the RISE Lab at UC Berkeley. Morteza received his...
Large Language Models in Computational Biology - A Primer
Просмотров 3,1 тыс.Год назад
SPEAKER: Shaoheng Liang is a computer scientist/bioinformatician and a Lane Fellow at Carnegie Mellon University in the Department of Computational Biology, working with Dr. Jian Ma. Before that, he worked at NuProbe USA, Inc., a company innovating cutting-edge sequencing solutions for disease detection. Liang earned his Ph.D. degree from Rice University, Department of Computer Science. Co-advi...
Bridging Data and Physics for Visual Content Creation in the 3D World
Просмотров 114Год назад
SPEAKER: Shenlong Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, specializing in computer vision and robotics. His research focuses on creating a digital replica of the world and simulating realistic new content to train and validate autonomous systems. Shenlong’s past work received the 2020 IROS Best Application Award Finali...
Lifting 2D Image Models to 3D
Просмотров 337Год назад
SPEAKER: Greg Shakhnarovich is a professor at Toyota Technological Institute at Chicago (TTIC), a philanthropically-endowed academic computer science institute located on the University of Chicago campus. He received a BSc degree in Mathematics and Computer Science from Hebrew University, Jerusalem, in 1994, an MSc degree in Computer Science from the Technion, Haifa, in 2001, and a PhD degree i...
TBD: Towards aBridging Diffusion-models
Просмотров 130Год назад
SPEAKER: Alex Schwing is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign and affiliated with the Coordinated Science Laboratory and the Computer Science Department. Prior to that, he was a postdoctoral fellow in the Machine Learning Group at University of Toronto collaborating with Raquel Urtasun, Rich Zemel, and Rus...
On Theoretical Foundations of Diffusion Models
Просмотров 176Год назад
SPEAKER: Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in Electrical Engineering. He has held research positions with Northwestern, the University of Illinois Urbana-Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, he has returned to UIUC, where he is currently a Profe...
Generative AI and Mental Health
Просмотров 391Год назад
SPEAKER: Logan Stapleton is a PhD student in Computer Science at the University of Minnesota and a member of GroupLens at University of Minnesota, advised by Profs. Haiyi Zhu and Steven Wu. His research studies how people use machine learning models in care work, such as child welfare and suicide prevention. Much of his work focuses on understanding how people are marginalized, discriminated ag...
All-in-One: Unifying Generative and Discriminative AI in the 4D Open World
Просмотров 375Год назад
SPEAKER: Yuxiong Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign. He is also affiliated with the National Center for Supercomputing Applications (NCSA). He received a Ph.D. in robotics from Carnegie Mellon University. His research interests lie in computer vision, machine learning, and robotics, with a particular focus on few-s...
Deep Fakes and Generated and Manipulated Media: It is Real and Coming for our Society!
Просмотров 53Год назад
Deep Fakes and Generated and Manipulated Media: It is Real and Coming for our Society!
Toward a Practical Theory of Deep Learning: Feature Learning in Infinitely Wide Networks
Просмотров 184Год назад
Toward a Practical Theory of Deep Learning: Feature Learning in Infinitely Wide Networks
Replicating and Auditing Black-Box Language Models
Просмотров 323Год назад
Replicating and Auditing Black-Box Language Models
Mitigating Harms of GenAI using Adversarial Tools
Просмотров 150Год назад
SPEAKER: Ben Zhao is a Neubauer Professor of Computer Science at University of Chicago. He completed his Ph.D. at UC Berkeley (2004) and B.S. from Yale (1997). He is a Fellow of the ACM, and a recipient of the NSF CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), USENIX Internet Defense Prize, ComputerWorld Magazine's Top 40 Technology Innovators award, IEEE ITC Ear...
Panel Discussion - What Do The Successes of Generative AI Mean For AI In General?
Просмотров 243Год назад
Moderator: Julia Hockenmaier (University of Illinois Urbana-Champaign) Panelists: Arindam Banerjee (University of Illinois Urbana-Champaign) Katie Driggs-Campbell (University of Illinois Urbana-Champaign) Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) Svetlana Lazebnik (University of Illinois Urbana-Champaign)
What Do Image Generators “Know?”
Просмотров 124Год назад
SPEAKER: David Forsyth (Fellow, IEEE) is the Fulton Watson Copp Chair in Computer Science at the University of Illinois Urbana-Champaign. He received the BSc and MSc degrees in electrical engineering from the University of the Witwatersrand, Johannesburg, and the MA and DPhil degrees from Oxford University. He has published more than 170 papers on computer vision, computer graphics, and machine...
Open Vocabulary Recognition with Large Text-to-Image Foundational Model
Просмотров 75Год назад
SPEAKER: Shalini De Mello is a Director of Research, New Experiences at NVIDIA, where she leads a team on AI-Mediated Reality and Interaction research. Prior to this, she was a Distinguished Research Scientist in the Learning and Perception Research Group at NVIDIA, which she joined in 2013. Her research interests are in AI, computer vision, and digital humans. She is interested in how AI can r...
Algorithmic Systems in Education
Просмотров 45Год назад
SPEAKER: Karrie Karahalios is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign where she heads the Social Spaces Group. Her work focuses on the interaction between people and the social cues they perceive in networked electronic spaces. Of particular interest are interfaces for pubic online and physical gathering spaces such as chatrooms, cafes, parks, e...
Sewoong Oh: Do Meta-learning and Federated Learning Find Good Representations?
Просмотров 8492 года назад
Sewoong Oh: Do Meta-learning and Federated Learning Find Good Representations?
Richard Y. Zhang: Improved Adversarial Attacks and Certified Defenses via Nonconvex Relaxations
Просмотров 3052 года назад
Richard Y. Zhang: Improved Adversarial Attacks and Certified Defenses via Nonconvex Relaxations
Gauri Joshi: Tackling Computational and Data Heterogeneity in Federated Learning
Просмотров 1,5 тыс.2 года назад
Gauri Joshi: Tackling Computational and Data Heterogeneity in Federated Learning
Three Recent Algorithmic Works on Online and Matching-Based Market Design
Просмотров 1642 года назад
Three Recent Algorithmic Works on Online and Matching-Based Market Design
The Role of Data in Power System Operations
Просмотров 1292 года назад
The Role of Data in Power System Operations
Data, the Fundamental Particle of Economics
Просмотров 932 года назад
Data, the Fundamental Particle of Economics
Learning Product Rankings Robust to Fake Users
Просмотров 762 года назад
Learning Product Rankings Robust to Fake Users
Limits of an Information Intermediary in Auction Design
Просмотров 632 года назад
Limits of an Information Intermediary in Auction Design
From Offline Greedy Algorithms to Online Learning: Theory and Applications
Просмотров 1022 года назад
From Offline Greedy Algorithms to Online Learning: Theory and Applications
Measuring Under-Reporting and Differential Response in Resident Crowdsourcing
Просмотров 582 года назад
Measuring Under-Reporting and Differential Response in Resident Crowdsourcing
Fascinating stuff, thanks
amazing work
Great
perfect profoser
Hello thank you for this video is there a possibility to get the article ?
Bro I'm planning to do my master's in Cyber Security. Will this topic be helpful when I'm covering the syllabus?
Thanks for sharing
So so abstract
Thank you for the video! Listening while working!
Interesting. Where can I read the work that lies behind the figures at about 12 min in?
Insightful
wondering how easy it is too disable these drones and take the packages . Africa a bad country for theft .
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Thanks for uploading this video, I always love a behind the scenes look with more Eng and Tech talk.
How to involve data variations caused by power flow change?
you can update the graph with a GNN. That would propagate these updates throughout it.
@@slavrine Very interesting ideas. From my understanding, an usable scenario is to detect wrong measurements like P, Q, U, phase of power flow, which is not uncommon in practice. But why not simply use traditional method like "state estimation" based on impedance models to detect unreasonable edge or node data?
𝕡𝐫ｏ𝕄ｏ𝔰𝓶
Predecting high resolution results using ML+ low resolution mesh seems interesting to me
How do I get hired?
So you're saying diversity is not a strength?
Do we need historical data to represent our states for reinforcement learning agent?
The Earth is cooler with the atmos/GHGs/albedo not warmer. To perform as advertised the GHGs require “extra” energy upwelling from the surface radiating as a black body. (ruclips.net/video/0Jijw7-YG-U/видео.html) The kinetic heat transfer processes of the contiguous atmos molecules render that scenario impossible. No greenhouse effect, no GHG warming, no man/CO2 driven climate change or Gorebal warming.
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Guy seems like a Neo-Gastonist
No, there are many Johns Doyles. Google for another one.
Oh, oh, I know this one!
Nice!
Bird strikes?
Are the slides available anywhere for this workshop ? Thanks for any suggestions.
I am a PhD candidate at the University of Kentucky, USA. I am interested in extending these ideas to power system distribution state estimation. Are you aware of any research in this area? If so, please let me know where I may find any papers and/or presentations in this area. So far I have applied feed forward fully connected multi-layer perceptron and convolutional neural networks. This discussion helps explain limitations of these two fully data driven approaches. Any information would be greatly appreciated! I will definitely be following your work going forward.
Great discussion! Are slides available for this presentation?
Thank you, glad you enjoyed the talk! The speaker's slides have not yet been posted, but they will be. Please check the workshop page of the C3.ai DTI website later: c3dti.ai/events/workshops/machine-learning-for-carbon-free-electricity/ Thanks!
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Stupid as all hell
the first question is purely from a scoreboard chaser, she might believe Einstein should get GRT empirically proved before he can publish his work.
Interesting presentatin! Where could I download the slides?
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İts sooo boreed.I'am never loss my time.
A new complete manuscript about the presented work can be found: arxiv.org/abs/2105.10446
Good presentation. THX
Changing the world by unlocking endless impossibilities. Great work
Great job!
uhmm. uhmm
What have you contributed to the world other than passing methane gas as you sit at the computer screen? We'll all wait here as you gather your thoughts.... We're still waiting... And waiting... Yup. We didn't think you had anything to contribute. Feel better about yourself now?
It's so inspiring! Go Zipline
That's great work and technology.......
Amazing and beautiful!!
Awesome 👏🏽 information in this digital age 🥂👀
You can throw sparse mutations at GPUs that have the full neural model and part of the training data each. Get the costs back and then send a global accept or reject message. There is not much data movement. Continuous Gray Code Optimization mutations are good. Anyway if you need very fast nets then fast-transform fixed_filter_bank nets are an option.
OK thank you
You can't get overfitting in large networks so easily. If one neuron over expresses that may correct errors related to one forward dot product but it will make the errors related to many other connected forward dot products worse. The training algorithm will stomp on the over expressing neuron. Hence no neuron can be exceptional and the search is limited to small subset of statictical solutions that require neurons to work in groups. You can see this very clearly in fast transform fixed-filter-bank neural networks.
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