generative model vs discriminative model
understanding difference between generative model and discriminative model with simple example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
Truth Conditional Meaning in Model Theory (Fragment F1) - Semantics in Linguistics
We introduce the model theory of fragment F1 in Chierchia and McConnel-Ginet (2000)'s book on #semantics in #linguistics. We cover the meaning of proper nouns, intransitive verbs, transitive verbs, negation, and conjunctions, as well as how to derive meaning of larger constituents. We do a
From playlist Semantics in Linguistics
Causal Behavioral Modeling Framework - Discrete Choice Modeling of Consumer Demand
There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact
From playlist Fundamentals of Machine Learning
Relevance model 1: Bernoulli sets vs. multinomial urns
[http://bit.ly/RModel] Relevance model is the language model of the relevant class. In this video we look at the difference between the multinomial model (the one used in relevance models) and the multiple-Bernoulli model, which forms the basis for the classical probabilistic models.
From playlist IR18 Relevance Model
Probabilistic model 8: modelling word frequencies
[http://bit.ly/BM-25] The classic probabilistic model of IR represents words as Bernoulli random variables (they either occur or not). How can we model term frequencies in the model? We look at the Poisson model and see that by itself it is not a good choice, as it cannot capture the burs
From playlist Probabilistic Model of IR
In this video, I define the notion of adjugate matrix and use it to calculate A-1 using determinants. This is again beautiful in theory, but inefficient in examples. Adjugate matrix example: https://youtu.be/OFykHi0idnQ Check out my Determinants Playlist: https://www.youtube.com/playlist
From playlist Determinants
Set Distribution Networks: a Generative Model for Sets of Images (Paper Explained)
We've become very good at making generative models for images and classes of images, but not yet of sets of images, especially when the number of sets is unknown and can contain sets that have never been encountered during training. This paper builds a probabilistic framework and a practic
From playlist Papers Explained
How to Program the Many Cores For Inconsistency Robustness
(January 12, 2011) Carl Hewitt gives a presentation addressing the current state of Moore's Law and looks at how Alan Turing's Model of Computation relates to this. He shows how this law can be applied to small system as well as very small systems. Stanford University: http://www.stanfor
From playlist Engineering
Model Theory - part 07 - Semantics pt 1
This is the first video on semantics.
From playlist Model Theory
R - Behavioral Profiles and Clustering
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class. This video focuses on behavioral profiles and cluster analysis to help understand categories and their features. Note: these videos are part of liv
From playlist Human Language (ANLY 540)
R - Conditional Inference Trees and Random Forests
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class. This video covers the more on collocations (words paired together) using conditional inference trees and random forests. Note: these videos are par
From playlist Human Language (ANLY 540)
Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class - this video set covers the updated version with both R and Python. This video covers cluster analysis focusing on how to group together features of
From playlist Human Language (ANLY 540)
Python - Building Feature Grammars Part 1
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This chapter covers how to write your own feature grammar using Python and nltk. You will learn what a feature grammar is, the ins and outs of how to define features and their components, and how to write
From playlist Natural Language Processing
AI Weekly Update - March 29th, 2021 (#30)!
Thank you for watching! Please Subscribe! Content Links: Recursive Classification: https://ai.googleblog.com/2021/03/recursive-classification-replacing.html Industrial Assembly via RL: https://arxiv.org/pdf/2103.11512.pdf Model-based RL in Healthcare: https://twitter.com/christina_x_ji/st
From playlist AI Research Weekly Updates
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer/Fall 2016 PSY 523 Psychology and Language lectures covering material from Harley's The Psychology of Language: From Data to Theory. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdo
From playlist PSY 523 Psychology and Language
Rasa Reading Group: Right for the Wrong Reasons
This week we'll be reading "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference" by Tom McCoy, Ellie Pavlick and Tal Linzen which was published at ACL 2019. Link to paper: https://www.aclweb.org/anthology/P19-1334/ Learn more about Rasa: https://ra
From playlist Rasa Reading Group
Bas Spitters: Modal Dependent Type Theory and the Cubical Model
The lecture was held within the framework of the Hausdorff Trimester Program: Types, Sets and Constructions. Abstract: In recent years we have seen several new models of dependent type theory extended with some form of modal necessity operator, including nominal type theory, guarded and c
From playlist Workshop: "Types, Homotopy, Type theory, and Verification"
