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
This E-Lecture discusses the fundamental ideas of generative grammar, the most influential grammar model in linguistic theory. In particular we exemplfy the main principles that account for the non-finite character of natural language as well as the phenonemon of native speaker competence.
From playlist VLC206 - Morphology and Syntax
(ML 13.5) Generative process specification
A compact way to specify a model is by its "generative process", using a convenient convention involving the graphical model.
From playlist Machine Learning
SYN104 - Unit Advice (Generative Grammar)
This short videoclip introduces the central goals of the respective VLC-E-Learning unit and provides some guidance as to how to proceed. More on http://www.linguistics-online.com
From playlist VLC206 - Morphology and Syntax
An Overview of Relations and Functions for Linguists - Semantics in Linguistics
In this video on #semantics in #linguistics we introduce relations and functions, talk about properties of relations and types of functions. Join this channel to get access to perks: https://www.youtube.com/channel/UCGYSfZbPp3BiAFs531PBY7g/join Instagram: http://instagram.com/TrevTutorOf
From playlist Semantics in Linguistics
Computational Semantics: How Computers Know what Words Mean [Lecture]
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://boydgraber.org/teaching/CMSC_723/ (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review
From playlist Computational Linguistics I
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
NOUN PHRASES - ENGLISH GRAMMAR
We discuss noun phrases. Noun phrases consist of a head noun, proper name, or pronoun. Noun phrases can be modified by adjective phrases or other noun phrases. Noun phrases take determiners as specifiers. We also draw trees for noun phrase. you want to support the channel, hit the "JOIN"
From playlist English Grammar
CMU Neural Nets for NLP 2017 (14): Neural Semantic Parsing
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * What is Graph-based Parsing? * Minimum Spanning Tree Parsing * Structured Training and Other Improvements * Dynamic Programming Methods for Phrase Structure Parsing * Reranking Slides: http:/
From playlist CMU Neural Nets for NLP 2017
Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Potts Pr
From playlist Stanford CS224U: Natural Language Understanding | Spring 2019
Paola Cantù : Logic and Interaction:pragmatics and argumentation theory
HYBRID EVENT Recorded during the meeting "Logic and transdisciplinarity" the February 11, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiov
From playlist Logic and Foundations
Understanding Semantic Web and its Applications by Asha Subramanian
PROGRAM SUMMER SCHOOL FOR WOMEN IN MATHEMATICS AND STATISTICS (ONLINE) ORGANIZERS: Siva Athreya (ISI-Bengaluru, India), Purvi Gupta (IISc, India), Anita Naolekar (ISI-Bengaluru, India) and Dootika Vats (IIT-Kanpur, India) DATE: 14 June 2021 to 25 June 2021 VENUE: ONLINE The summer sch
From playlist Summer School for Women in Mathematics and Statistics (ONLINE) - 2021
In this E-Lecture, Prof. Handke outlines the basic methods and principles of historical semantics. He discusses the role of word etymologies, he defines the central mechanisms of semantic change and lists some examples of lexical change.
From playlist VLC103 - The Nature of Meaning
This second E-Lecture about word semantics discusses the main sense relations, i.e. the relations between the lexemes of a language using numerous examples. This includes the treatment of concepts such as markedness and sense in general.
From playlist VLC103 - The Nature of Meaning
SEM114 - Theories of Word Meaning
In this E-Lecture Prof. Handke discusses several approaches towards the definition of word meaning, among them semantic fiels, componential analysis, meaning postulates and cognitive approaches, such as semantic networks and frames.
From playlist VLC103 - The Nature of Meaning
Simplified Machine Learning Workflows with Anton Antonov, Session #6: Semantic Analysis (Part 1)
Anton Antonov, a senior mathematical programmer with a PhD in applied mathematics, live-demos key Wolfram Language features that are very useful in machine learning. In this session, he discusses the Latent Semantic Analysis Workflows. Notebook materials are available at: https://wolfr.am
From playlist Simplified Machine Learning Workflows with Anton Antonov
Predicting aesthetic appreciation of images. - Murray - Workshop 3 - CEB T1 2019
Naila Murray (Naver) / 02.04.2019 Predicting aesthetic appreciation of images. Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision communi
From playlist 2019 - T1 - The Mathematics of Imaging
Negativity and semantic change - Will Hamilton, Stanford University
It is often argued that natural language is biased towards negative differentiation, meaning that there is more lexical diversity in negative affectual language, compared to positive language. However, we lack an understanding of the diachronic linguistic mechanisms associated with negativ
From playlist Turing Seminars
An Overview of Predicate Logic for Linguists - Semantics in Linguistics
This video covers predicate logic in #semantics for #linguistics. We talk about predicates, quantifiers (for all, for some), how to translate sentences into predicate logic, scope, bound variables, free variables, and assignment functions. Join this channel to get access to perks: https:/
From playlist Semantics in Linguistics