Knowledge graph embedding

First look at Knowledge Graph Embedding (w/ simple Jupyter NB dgl-ke)

Knowledge Graph Embedding and its advantages for answering search queries. Simple explanation of Knowledge Graph Embedding and its use case. Tech to answer your (Siri) questions is basically a Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph (KG) embeddings library built

From playlist Learn Graph Neural Networks: code, examples and theory

Knowledge Graph Embedding - Dec 2021

An intro to Knowledge Graphs, based on our knowledge of Graph Neural Networks. A simple example provides an easy pathway to Knowledge Graphs and training of Knowledge Graphs (AI). Knowledge graphs (KG) are data structures that store information about different entities (nodes) and their

From playlist Learn Graph Neural Networks: code, examples and theory

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)

Lecture13. Graph Embeddings

Network Science 2021 @HSE

From playlist Network Science, 2021

CS224W: Machine Learning with Graphs | 2021 | Lecture 3.1 - Node Embeddings

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cv1BEU Jure Leskovec Computer Science, PhD From previous lectures we see how we can use machine learning with feature engineering to make predictions on nodes, li

From playlist Stanford CS224W: Machine Learning with Graphs

Embedding Graphs with Deep Learning

This video explains how to Embed Graphs with Deep Learning. This includes showing the difference between Matrix Decomposition and Deep learning methods as well. Thanks for watching! www.henryailabs.com

From playlist Deep Learning on Graphs

Word Embeddings

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

Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 15 - Add Knowledge to Language Models

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/31fNyFN To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course

From playlist Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021

CS224W: Machine Learning with Graphs | 2021 | Lecture 3.3 - Embedding Entire Graphs

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/316zi1Z Jure Leskovec Computer Science, PhD In some scenarios it is important to not only learn embeddings for nodes, but also the entire graph. In this video, we

From playlist Stanford CS224W: Machine Learning with Graphs

Knowledge Graph for a Medical Application - DEMO in Python

Watch a real-world coding example of official DGL on a Knowledge Graph for medical research. Understand in real-time why a Graph Neural Network is so important to gain insight in complex data sets, highlighting a heterogeneous Knowledge Graph in DGL code. All Credits to: A team of AWS sci

From playlist Learn Graph Neural Networks: code, examples and theory

Lecture16. Knowledge graphs

Network Science 2021 @ HSE

From playlist Network Science, 2021

Knowledge Graphs for Drug Repurposing (live stream)

Vassilis Ioannidis presents his team's work at AWS on open-sourcing a biological knowledge graph to fight COVID-19. The problem of drug repurposing is discussed in the context of knowledge graph representation learning. You can find Vassilis at his website (https://sites.google.com/site/

From playlist Graph Neural Networks

[GATA] Learning Dynamic Belief Graphs to Generalize on Text-Based Games | AISC

For slides and more information on the paper, visit https://ai.science/e/gata-learning-dynamic-belief-graphs-to-generalize-on-text-based-games--Ubf3kPJc5FKPer1s3BhH Speaker: Pascal Poupart; Host: Susan Shu Chang Motivation: Playing text-based games requires skills in processing natural

From playlist Reinforcement Learning

CS224W: Machine Learning with Graphs | 2021 | Lecture 19.1 - Pre-Training Graph Neural Networks

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3mnajzE Jure Leskovec Computer Science, PhD There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution predi

From playlist Stanford CS224W: Machine Learning with Graphs

CS224W: Machine Learning with Graphs | 2021 | Lecture 11.3 - Query2box: Reasoning over KGs

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3bngZHH Lecture 11.3 - Query2box Reasoning over KGs Using Box Embeddings Jure Leskovec Computer Science, PhD In this video, we show how to answer more complex quer

From playlist Stanford CS224W: Machine Learning with Graphs

AWS CORD-19 Search: A Neural Search Engine and Knowledge Graph for COVID-19 Literature

Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/​ Watch all Healthcare NLP Summit 2021 sessions: https://www.nlpsummit.org/​ With the global outbreak of Coronavirus, p

From playlist Healthcare NLP Summit 2021

Learn low-dim Embeddings that encode GRAPH structure (data) : "Representation Learning" /arXiv

Optimize your complex Graph Data before applying Neural Network predictions. Automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. An encoder-decoder perspective, random walk approach

From playlist Learn Graph Neural Networks: code, examples and theory