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PROBPROG Conference
เข้าร่วมเมื่อ 1 พ.ย. 2018
Information about accessibility can be found at accessibility.mit.edu/
Jan Kudlicka: Probabilistic Programming for birth-death models of evolution...
Jan Kudlicka: Probabilistic Programming for birth-death models of evolution...
มุมมอง: 278
วีดีโอ
Yuan Zhou: Divide, Conquer, Combine: a New Inference Strategy...with Stochastic Support
มุมมอง 1433 ปีที่แล้ว
Yuan Zhou: Divide, Conquer, Combine: a New Inference Strategy...with Stochastic Support
Panel: Probabilistic Programming in the Field - Bayesian Data Modeling
มุมมอง 2903 ปีที่แล้ว
Moderator & Panelist: Veronica Weiner (MIT) Other panelists: Nimar Arora (Facebook), Daniel Lee (Generable), Lawrence Murray (Uber AI), Rif A. Saurous (Google), Ulrich Schaechtle (MIT)
Panel discussion: Complex simulators & potential industry applications of probabilistic programming
มุมมอง 1573 ปีที่แล้ว
Moderator: Veronica Weiner (MIT) Panelists: Nimar Arora (Facebook), Ashish Kapoor (Microsoft), Atilim Gunes Baydin (Oxford), Tejas Kulkarni (Common Sense Machines)
Robert Osazuwa Ness: "Towards causal inference with latent variable models and programs"
มุมมอง 2563 ปีที่แล้ว
Robert Osazuwa Ness: "Towards causal inference with latent variable models and programs"
Maria Gorinova: "Efficient inference with discrete parameters in Stan"
มุมมอง 2953 ปีที่แล้ว
Maria Gorinova: "Efficient inference with discrete parameters in Stan"
Ben Zinberg: "Structured, differentiable models of 3D scenes via Generative Scene Graphs"
มุมมอง 1813 ปีที่แล้ว
Ben Zinberg: "Structured, differentiable models of 3D scenes via Generative Scene Graphs"
Zenna Tavares: "A Language for Counterfactual Generative Models"
มุมมอง 2743 ปีที่แล้ว
Zenna Tavares: "A Language for Counterfactual Generative Models"
Yura Perov: "MultiVerse: Causal Reasoning in Probabilistic Programming using Importance Sampling"
มุมมอง 1393 ปีที่แล้ว
Yura Perov: "MultiVerse: Causal Reasoning in Probabilistic Programming using Importance Sampling"
Hugo Paquet: "Densities of almost-surely terminating probabilistic programs are differentiable..."
มุมมอง 423 ปีที่แล้ว
Hugo Paquet: "Densities of almost-surely terminating probabilistic programs are differentiable..."
Daniel Ritchie: "Learning Neurosymbolic 3D Models"
มุมมอง 2583 ปีที่แล้ว
Daniel Ritchie: "Learning Neurosymbolic 3D Models"
Leslie Kaelbling: "Doing for our robots what nature did for us"
มุมมอง 5063 ปีที่แล้ว
Leslie Kaelbling: "Doing for our robots what nature did for us"
David Chiang:"Translating Recursive Probabilistic Programs to Factor Graph Grammars"
มุมมอง 1533 ปีที่แล้ว
David Chiang:"Translating Recursive Probabilistic Programs to Factor Graph Grammars"
Christine Tasson: "Probabilistic Programming and Semantics"
มุมมอง 1403 ปีที่แล้ว
Christine Tasson: "Probabilistic Programming and Semantics"
Iris Seaman: "Nested reasoning about autonomous agents using probabilistic programs"
มุมมอง 1003 ปีที่แล้ว
Iris Seaman: "Nested reasoning about autonomous agents using probabilistic programs"
Zoubin Ghahramani: "Probabilistic Machine Learning: From theory to industrial impact"
มุมมอง 6K6 ปีที่แล้ว
Zoubin Ghahramani: "Probabilistic Machine Learning: From theory to industrial impact"
Angelika Kimmig: "A short introduction to probabilistic logic programming"
มุมมอง 3.3K6 ปีที่แล้ว
Angelika Kimmig: "A short introduction to probabilistic logic programming"
Brooks Paige: "Semi-interpretable probabilistic models"
มุมมอง 1.5K6 ปีที่แล้ว
Brooks Paige: "Semi-interpretable probabilistic models"
Dan Roy: "Algorithmic Barriers to Representing Conditional Independence"
มุมมอง 1.4K6 ปีที่แล้ว
Dan Roy: "Algorithmic Barriers to Representing Conditional Independence"
Kristian Kersting: "Democratizing Machine Learning using Probabilistic Programming"
มุมมอง 1K6 ปีที่แล้ว
Kristian Kersting: "Democratizing Machine Learning using Probabilistic Programming"
Michael Tingley: "Probabilistic programming @ FB"
มุมมอง 1K6 ปีที่แล้ว
Michael Tingley: "Probabilistic programming @ FB"
Eric Atkinson: "Verifying Handcoded Probabilistic Inference Procedures"
มุมมอง 1786 ปีที่แล้ว
Eric Atkinson: "Verifying Handcoded Probabilistic Inference Procedures"
Adam Scibior: "Denotational account of approximate Bayesian inference"
มุมมอง 4606 ปีที่แล้ว
Adam Scibior: "Denotational account of approximate Bayesian inference"
Chung-Chieh Shan: "Calculating distributions"
มุมมอง 5976 ปีที่แล้ว
Chung-Chieh Shan: "Calculating distributions"
Good
David Blei is the Woody Allen of the academics.
what do you mean with that?
is he a pdf file?
This will not end well. Can we PLEASE CeaseAi -GPT?
Thanks for sharing the great talk, but the video has got no sound after 17mins...
The power of search is amazing, i have no clue how i got here
Hi. When can I expect the PROBPROG 2021 talks to be uploaded?
The angriest talking person ever in a conference.
I love how confident this guy is. Knowing that he's right, no matter the size of audience or number of questions. Wonder what progress they made since.
Such cool work 🤓
Wow, this is incredible!
One of the best talks I have seen on this :)
I know he's tackling the Occam Razor version of this but I wonder how this could be extended to perform on more difficult coding problems by incorporating expert knowledge
Expert knowledge can be incorporated already. Just add concepts to the library manually.
Can anyone share an explanation about 'solver' referred in the video? What is the distinction between a model and a solver they were talking about?
A model declaratively establishes a relationship between variables without offering a way to find the value (or distribution) of variables. A solver takes a model (and possibly some data) and does that. For example, a quadratic equation x^2 + x + 1 = 0 is a model, while the Bhaskara method is a solver for that kind of model that determines the value of x.
@@rodrigobraz2 Thanks! Although it is quite intuitive, I could not be sure.
"DL community may be repeating history... #BOFAI (Bad Old Fashioned AI)" @12:30 epic line ;)
Good shit! :)
fantastic
Thanks
A very good description of BBVI.
where is noah's talk?
11:27 Saad feeling very sad
This lecture is gold and very unique. Deserves way more view
agree
"Statistics is mathematics of data." -- Zoubin Ghahramani
Great talk by Yordan! I am eager to learn more about the Infer.NET framework and some of the new models, Alexandria for example, is new to me.
Dave Blei humor is the best humor. "20 minutes? We'll just start again" "That is handwavy even by my own standards"
This guy is taking a stab at Josh's hard problem of learning, and the video has 2 views after 2 weeks. People on this planet have no taste about what is interresting
Lol!
I watched Yannic's video on DreamCoder ( th-cam.com/video/qtu0aSTDE2I/w-d-xo.html ) and only got to the actual paper a few weeks later. Seeing this one is from 2018, I am late by 2+ years. What should I follow to not wait for 3 years before stuff like this gets public attention?
My go at the learning = programming idea. (very related to the Noah Goodman's Probabilistic Language of Thought Hypothesis) Please correct me if I'm wrong Tuning parameters of existing code: not sure Writing new code: "normal learning" where you learn new things, mechanisms, relations, dynamics by experience. Extending or fixing code: Learning from mistakes. Eg: your tennis backhand sucks and the trainer tells you how to fix it, or you take an exam and review your wrong answers Rewriting/Refactoring a library of code: re-learning? Like Jaynes saying that a lack of statistics background is a good thing because there will be less to unlearn? Having to re-consider your entire perspective on a topic. Adapting code written for other purposes: Analogy making. Eg using what we know about coding to understand about human learning like we do now Getting code from other people or published sources: human language, reading, formal education. That's why books are so powerful: you go from having to write everything from scratch to just pip install everything. (or rather gem install for the real hackers) Debugging: thought experiments or regular experiments to find our where your are wrong. Testing hypotheses. Asking the right questions. Rewriting existing code in a different language: not sure Compiling code (translating from interpretable source code -> efficient machine code): training / growing myelin sheaths Writing a new language or compiler: meditation? not sure
Infer.NET is now open source at github.com/dotnet/infer