OpenGL - 3D rendering overview
Part of a series covering OpenGL. (revision of an earlier video: some restructuring and narration fixes)
From playlist OpenGL
Stéphane Mallat - Multiscale Models for Image Classification and Physics with Deep Networks
Abstract: Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning, image processing, physics and mathematics. Deep convolutional networks are able to approximate such functionals over a wide range of applications. This talk shows that t
From playlist 2nd workshop Nokia-IHES / AI: what's next?
Reduced Order Modeling Using Machine Learning
Learn how to create reduced-order models of high-fidelity systems using machine learning techniques in System Identification Toolbox™. Watch a demonstration on how to identify support vector machine–based nonlinear ARX models using measurements from a high-fidelity model of an internal c
From playlist Reduced Order Modeling
Isotonic regression in general dimensions – Richard Samworth, University of Cambridge
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions
Approximation of generalized ridge functions in high dimensions – Sandra Keiper
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions
Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video gives an example of multilevel modeling in R - covers data screening in wide format, melting to long format, nlme for analysis, and interpretation of predictors. Lecture materials and assignments available at statisticsofdoom.
From playlist Advanced Statistics Videos
Parsimonious Representations in data science - Dr Armin Eftekhari, University of Edinburgh
Every minute, humankind produces about 2000 Terabytes of data and learning from this data has the potential to improve many aspects of our lives. Doing so requires exploiting the geometric structure hidden within the data. Our overview of models in data and computational sciences starts wi
From playlist Data science classes
A high level view of reduced order modeling for plasmas
Plasma physics relies on a hierarchy of modeling with successive approximations in order to efficiently simulate plasmas and use real-time control on real-world plasma devices. Here, we provide a high level view of our recent work that attempts to build a bridge between the many magnetohyd
From playlist Research Abstracts from Brunton Lab
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
This video by Kathleen Champion describes a new approach for simultaneously discovering models and an effective coordinate system using a custom SINDy autoencoder. Paper at PNAS: https://www.pnas.org/content/116/45/22445.abstract Kathleen Champion, Bethany Lusch, J. Nathan Kutz, Steven L
From playlist Research Abstracts from Brunton Lab
Dimensionality Reduction | Stanford CS224U Natural Language Understanding | Spring 2021
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To learn more about this course visit: https://online.stanford.edu/courses/cs224u-natural-language-understanding To follow along with the course schedule and sy
From playlist Stanford CS224U: Natural Language Understanding | Spring 2021
How to code BERT Word + Sentence Vectors (Embedding) w/ Transformers? Theory + Colab, Python
Before SBERT there was BERT. A stacked Encoder of a Transformer, bidirectional. I show you in theory (2min) and in code (Colab) how to build WORD Embeddings (word vectors) form the hidden states of each of the 12 BERT encoders and how to build a SENTENCE Vector (a Sentence embedding) from
From playlist BERT Transformers - Word and Sentence Vectors /Embedding
29th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, June 23, 2021, 10:00am Eastern Time Zone (US & Canada) Speaker: Paul Hand Title: Signal Recovery with Generative Priors Abstract: Recovering images from very few measurements is an important task in imaging problems. Doing so requires assuming a model of what makes some
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
Yoshua Bengio: "Representation Learning and Deep Learning, Pt. 5"
Graduate Summer School 2012: Deep Learning, Feature Learning "Representation Learning and Deep Learning, Pt. 5" Yoshua Bengio, University of Montreal Institute for Pure and Applied Mathematics, UCLA July 20, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/gr
From playlist GSS2012: Deep Learning, Feature Learning
AI Weekly Update - March 8th, 2021 (#27)!
Thank you for watching! Please Subscribe! Content Links: Multimodal neurons (OpenAI): https://openai.com/blog/multimodal-neurons/ Multimodal neurons (Distil): https://distill.pub/2021/multimodal-neurons/ DeepDream (Wikipedia): https://en.wikipedia.org/wiki/DeepDream CLIP (OpenAI): https:/
From playlist AI Research Weekly Updates
DDPS | Charting dynamics from data
In this DDPS talk from April 8, 2022, Daniel Floryan (University of Houston) presents recent work that fruitfully combines a classical idea from applied mathematics with modern methods of machine learning to learn minimal dynamical models directly from time series data. Description: We of
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning
Unsupervised learning is one of the three major branches of machine learning (along with supervised learning and reinforcement learning). It is also arguably the least developed branch. Its goal is to find a parsimonious description of the input data by uncovering and exploiting its hidden
From playlist Learning resources
Yann LeCun - Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)
#selfsupervisedlearning #yannlecun #facebookai Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money.
From playlist Papers Explained
Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters 0:00 Introduction 2:18 Improvements in Video Modeling 6:08 Vokenization 7:31 HowTo100M Data 9:07 Teacher Learning 13:06 Interesting Distillation Ideas 17
From playlist AI Weekly Update - July 15th, 2021!
Visualization of Quantum Physics (Quantum Mechanics)
This video visually demonstrates some basic quantum physics concepts using the simple case of a free particle. All the simulations here are based on real equations and laws. See more information here: https://www.udiprod.com/quantum-physics/ The mathematics involved was taken from this
From playlist Animated Physics Simulations