Jasna Urbančič (11/03/21):Optimizing Embedding using Persistence
Title: Optimizing Embedding using Persistence Abstract: We look to optimize Takens-type embeddings of a time series using persistent (co)homology. Such an embedding carries information about the topology and geometry of the dynamics of the time series. Assuming that the input time series
From playlist AATRN 2021
Word embeddings are one of the coolest things you can do with Machine Learning right now. Try the web app: https://embeddings.macheads101.com Word2vec paper: https://arxiv.org/abs/1301.3781 GloVe paper: https://nlp.stanford.edu/pubs/glove.pdf GloVe webpage: https://nlp.stanford.edu/proje
From playlist Machine Learning
Rasa Algorithm Whiteboard - Understanding Word Embeddings 1: Just Letters
We're making a few videos that highlight word embeddings. Before training word embeddings we figured it might help the intuition if we first trained some letter embeddings. It might suprise you but the idea with an embedding can also be demonstrated with letters as opposed to words. We're
From playlist Algorithm Whiteboard
Graph Neural Networks, Session 6: DeepWalk and Node2Vec
What are Node Embeddings Overview of DeepWalk Overview of Node2vec
From playlist Graph Neural Networks (Hands-on)
Rasa Algorithm Whiteboard - Implementation of Subword Embeddings
In this video we highlight how you might construct a neural network that can handle subword embeddings.
From playlist Algorithm Whiteboard
Rasa Algorithm Whiteboard - General Embeddings vs. Specific Problems
In this video, we'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much. They might cause an improvement but because they represent a general pattern in language ... you could wonder if the pattern applies to your specific use-case. So i
From playlist Algorithm Whiteboard
Rasa Algorithm Whiteboard - Understanding Word Embeddings 3: GloVe
In this video we're going to explore GloVe embeddings while also understanding why word embeddings are genuinely hard to understand. We'll train our own variant of these embeddings too and you can find the code for this on github as well as colab. This material can be found here; https:/
From playlist Algorithm Whiteboard
Symplectic Embeddings and Infinite Staircases - Nicole Magill
Joint IAS/Princeton University Symplectic Geometry Seminar Topic: Symplectic Embeddings and Infinite Staircases Speaker: Nicole Magill Affiliation: Cornell University Date: February 6, 2023 The four dimensional ellipsoid embedding function of a toric symplectic manifold M measures when a
From playlist Mathematics
Markus Haase : Operators in ergodic theory - Lecture 1 : Operators dynamics versus ...
Abstract : The titles of the of the individual lectures are: 1. Operators dynamics versus base space dynamics 2. Dilations and joinings 3. Compact semigroups and splitting theorems Recording during the thematic meeting : "Probabilistic Aspects of Multiple Ergodic Averages " the December 6
From playlist Jean-Morlet Chair - Lemanczyk/Ferenczi
BigScience BLOOM | 3D Parallelism Explained | Large Language Models | ML Coding Series
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this 4th video of the Large Language Model series I walk you through the BigScience's BLOOM model codebase! The main focus is on underst
From playlist Transformers
Laura Capuano: An effective criterion for periodicity of p-adic continued fractions
Abstract: It goes back to Lagrange that a real quadratic irrational has always a periodic continued fraction. Starting from decades ago, several authors proposed different definitions of a p-adic continued fraction, and the definition depends on the chosen system of residues mod p. It turn
From playlist Women at CIRM
#Word2Vec #SkipGram #CBOW #DeepLearning Word2Vec is a very popular algorithm for generating word embeddings. It preserves word relationships and is used with a lot of Deep Learning applications. In this video we will learn about the working of word2vec and word embeddings. We will also
From playlist Deep Learning with Keras - Python
OPT-175B: Open Pretrained Transformer | ML Coding Series
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this video I do a deep dive into the metaseq (Open Pretrained Transformer, OPT-175B) codebase. I first show you how to set up the code o
From playlist Transformers
MAST30026 Lecture 22: Urysohn's lemma
I gave the proof of Urysohn's lemma and briefly elaborated some of its important consequences. Given a pair of closed disjoint subsets of a normal topological space, the lemma asserts the existence of a real-valued continuous function on the space which takes the value 0 on the first close
From playlist MAST30026 Metric and Hilbert spaces
Markus Haase : Operators in ergodic theory - Lecture 2 : Dilations and joinings
Abstract : The titles of the of the individual lectures are: 1. Operators dynamics versus base space dynamics 2. Dilations and joinings 3. Compact semigroups and splitting theorems Recording during the thematic meeting : "Probabilistic Aspects of Multiple Ergodic Averages " the December 7
From playlist Dynamical Systems and Ordinary Differential Equations
Paper Read Aloud: Interactive Refinement of Cross-Lingual Word Embeddings
An experiment! I recorded this a while ago but didn't post it until now because ... 2020. A long time ago, a blind student once asked me to record myself reading my papers when he found that I do that anyway during my editing process, so I finally did it. This is an experiment, feedback
From playlist Papers Read Aloud
Transfer learning and Transformer models (ML Tech Talks)
In this session of Machine Learning Tech Talks, Software Engineer from Google Research, Iulia Turc, will walk us through the recent history of natural language processing, including the current state of the art architecture, the Transformer. 0:00 - Intro 1:07 - Encoding text 8:21 - Langu
From playlist ML & Deep Learning
Deep Learning 4: Beyond Image Recognition, End-to-End Learning, Embeddings
Raia Hadsell, Senior Research Scientist, discusses beyond image recognition, end-to-end learning, and embeddings as part of the Advanced Deep Learning & Reinforcement Learning Lectures. Slides in video contain images from: Shelhamer, Long, Darrell (2016) Fully Convolutional Networks for
From playlist Learning resources
Today we take a look at word representations and basic word embeddings including a usage example in Information Retrieval. Slides & transcripts are available at: https://github.com/sebastian-hofstaetter/teaching 📖 Check out Youtube's CC - we added our high quality (human corrected) transc
From playlist Advanced Information Retrieval 2021 - TU Wien