A Practical Introduction to Interpretations.
We give a definition that is necessary for the construction of Hodge Theaters.
From playlist Model Theory
Model Theory - part 01 - The Setup in Classical Set Valued Model Theory
Here we give the basic setup for Model Theory. I learned this from a talk Tom Scanlon gave in 2010 at CUNY.
From playlist Model Theory
On finite dimensional omega-categorical structures (...) - P. Simon - Workshop 1 - CEB T1 2018
Pierre Simon (Berkeley) / 31.01.2018 On finite dimensional omega-categorical structures and NIP theories The study of omega-categorical structures lies at the intersection of model theory, combinatorics and group theory. Some classes of omega-categorical structures have been classified,
From playlist 2018 - T1 - Model Theory, Combinatorics and Valued fields
Stable and NIP regularity in groups - G. Conant - Workshop 1 - CEB T1 2018
Gabriel Conant (Notre Dame) / 01.02.2018 We use local stability theory to prove a group version of Szemer´edi regularity for stable subsets of finite groups. Toward generalizing this result to the NIP setting, we consider definable set systems of finite VC-dimension in pseudofinite groups
From playlist 2018 - T1 - Model Theory, Combinatorics and Valued fields
Model Theory - part 04 - Posets, Lattices, Heyting Algebras, Booleans Algebras
This is a short video for people who haven't seen a Heyting algebras before. There is really nothing special in it that doesn't show up in wikipedia or ncatlab. I just wanted to review it before we use them. Errata: *at 3:35: there the law should read (a and (a or b) ), not (a and (a and
From playlist Model Theory
NIPS 2011 Sparse Representation & Low-rank Approximation Workshop: Online Spectral...
Sparse Representation and Low-rank Approximation Workshop at NIPS 2011 Invited Talk: Online Spectral Identification of Dynamical Systems by Byron Boots, Carnegie Mellon University
From playlist NIPS 2011 Sparse Representation & Low-rank Approx Workshop
DDPS | Machine Learning and Multi-scale Modeling
Description: Multi-scale modeling is an ambitious program that aims at unifying the different physical models at different scales for the practical purpose of developing accurate models and simulation protocols for properties of interest. Although the concept of multi-scale modeling is ver
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Model Theory - part 07 - Semantics pt 1
This is the first video on semantics.
From playlist Model Theory
Joan Bruna & Michael Bronstein Interview - Geometric Deep Learning
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk
From playlist Interviews
(ML 7.7) Dirichlet-Categorical model (part 1)
The Dirichlet distribution is a conjugate prior for the Categorical distribution (i.e. a PMF a finite set). We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
NIP Henselian fields - F. Jahnke - Workshop 2 - CEB T1 2018
Franziska Jahnke (Münster) / 05.03.2018 NIP henselian fields We investigate the question which henselian valued fields are NIP. In equicharacteristic 0, this is well understood due to the work of Delon: an henselian valued field of equicharacteristic 0 is NIP (as a valued field) if and on
From playlist 2018 - T1 - Model Theory, Combinatorics and Valued fields
NIPS 2011 Domain Adaptation Workshop: History Dependent Domain Adaptation
Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Talk: History Dependent Domain Adaptation by Allen Lavoie Abstract: We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predicti
From playlist NIPS 2011 Domain Adaptation Workshop
NIPS 2011 Learning Semantics Workshop: Learning Semantics of Movement
Learning Semantics Workshop at NIPS 2011 Invited Talk: Learning Semantics of Movement by Timo Honkela Abstract: In this presentation, we consider how to computationally model the interrelated processes of understanding natural language and perceiving and producing movement in multim
From playlist NIPS 2011 Learning Semantics Workshop
NIPS 2011 Music and Machine Learning Workshop: This is the Remix: Structural Improvisation...
International Music and Machine Learning Workshop: Learning from Musical Structure at NIPS 2011 Invited Talk: This is the Remix: Structural Improvisation using Automated Pattern Discovery by Sean Whalen Sean Whalen is a postdoctoral researcher in the IDS lab at Columbia University, f
From playlist NIPS 2011 Music and Machine Learning Workshop
NIPS 2011 Domain Adaptation Workshop: Training Structured Prediction Models
Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Talk: Training Structured Prediction Models with Extrinsic Loss Functions by Slav Petrov Slav Petrov is a Research Scientist at Google New York who works on problems at the intersection of natural language process
From playlist NIPS 2011 Domain Adaptation Workshop
Lecture 5 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. This course provides a broad introduction
From playlist Lecture Collection | Machine Learning
NIPS 2011 Music and Machine Learning Workshop: Modeling the Acoustic Structure of Musical Emotion...
International Music and Machine Learning Workshop: Learning from Musical Structure at NIPS 2011 Invited Talk: Modeling the Acoustic Structure of Musical Emotion with Deep Belief Networks by Erik M. Schmidt
From playlist NIPS 2011 Music and Machine Learning Workshop
David Tse: "How to Solve NP-hard Problems in Linear Time"
Computational Genomics Summer Institute 2017 Tutorial: "How to Solve NP-hard Problems in Linear Time" David Tse, Stanford University Institute for Pure and Applied Mathematics, UCLA July 12, 2017 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2017
NIPS 2011 Sparse Representation & Low-rank Approximation Workshop: Group Sparse Hidden Markov...
Sparse Representation and Low-rank Approximation Workshop at NIPS 2011 Invited Talk: Group Sparse Hidden Markov Models by Jen-Tzung Chien, National Cheng Kung University, Taiwan
From playlist NIPS 2011 Sparse Representation & Low-rank Approx Workshop
Sebastian Reich (DDMCS@Turing): Learning models by making them interact
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems