Artificial neural networks | Computational statistics
A residual neural network (ResNet) is an artificial neural network (ANN). It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. Skip connections or shortcuts are used to jump over some layers (HighwayNets may also learn the skip weights themselves through an additional weight matrix for their gates).Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. Models with several parallel skips are referred to as DenseNets. In the context of residual neural networks, a non-residual network may be described as a plain network. Like in the case of Long Short-Term Memory recurrent neural networksthere are two main reasons to add skip connections: to avoid the problem of vanishing gradients, thus leading to easier to optimize neural networks, wherethe gating mechanisms facilitate information flow across many layers ("information highways"), or to mitigate the Degradation (accuracy saturation) problem; where adding more layers to a suitably deep model leads to higher training error. During training, the weights adapt to mute the upstream layer, and amplify the previously-skipped layer. In the simplest case, only the weights for the adjacent layer's connection are adapted, with no explicit weights for the upstream layer. This works best when a single nonlinear layer is stepped over, or when the intermediate layers are all linear. If not, then an explicit weight matrix should be learned for the skipped connection (a HighwayNet should be used). Skipping effectively simplifies the network, using fewer layers in the initial training stages. This speeds learning by reducing the impact of vanishing gradients, as there are fewer layers to propagate through. The network then gradually restores the skipped layers as it learns the feature space. Towards the end of training, when all layers are expanded, it stays closer to the manifold and thus learns faster. A neural network without residual parts explores more of the feature space. This makes it more vulnerable to perturbations that cause it to leave the manifold, and necessitates extra training data to recover. A residual neural network was used to win the ImageNet 2015 competition, and has become the most cited neural network of the 21st century. (Wikipedia).
[Classic] Deep Residual Learning for Image Recognition (Paper Explained)
#ai #research #resnet ResNets are one of the cornerstones of modern Computer Vision. Before their invention, people were not able to scale deep neural networks beyond 20 or so layers, but with this paper's invention of residual connections, all of a sudden networks could be arbitrarily de
From playlist Papers Explained
Applied Machine Learning 2019 - Lecture 22 - Advanced Neural Networks
Residual Networks, DenseNet, Recurrent Neural Networks. Slides and materials on the course website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Neural Networks 1 Neural Units
From playlist Week 5: Neural Networks
Mattia G. Bergomi (8/29/21): Comparing Neural Networks via Generalized Persistence
Artificial neural networks are often used as black boxes to solve supervised tasks. At each layer, the network updates its representation of the dataset to minimize a given error function, depending on the correct assignment of predetermined labels to each observed data point. On the other
From playlist Beyond TDA - Persistent functions and its applications in data sciences, 2021
Multilayer Neural Networks - Part 1: Introduction
This video is about Multilayer Neural Networks - Part 1: Introduction Abstract: This is a series of video about multi-layer neural networks, which will walk through the introduction, the architecture of feedforward fully-connected neural network and its working principle, the working prin
From playlist Neural Networks
Principles of Riemannian Geometry in Neural Networks | TDLS
Toronto Deep Learning Series, 13 August 2018 For slides and more information, visit https://aisc.ai.science/events/2018-08-13/ Paper Review: https://papers.nips.cc/paper/6873-principles-of-riemannian-geometry-in-neural-networks.pdf Speaker: https://www.linkedin.com/in/helen-ngo/ Organiz
From playlist Math and Foundations
Practical 4.0 – RNN, vectors and sequences
Recurrent Neural Networks – Vectors and sequences Full project: https://github.com/Atcold/torch-Video-Tutorials Links to the paper Vinyals et al. (2016) https://arxiv.org/abs/1609.06647 Zaremba & Sutskever (2015) https://arxiv.org/abs/1410.4615 Cho et al. (2014) https://arxiv.org/abs/1406
From playlist Deep-Learning-Course
Recurrent Neural Networks - Ep. 9 (Deep Learning SIMPLIFIED)
Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-
From playlist Deep Learning SIMPLIFIED
DDPS | "When and why physics-informed neural networks fail to train" by Paris Perdikaris
Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such c
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Paris Perdikaris: "Overcoming gradient pathologies in constrained neural networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Overcoming gradient pathologies in constrained neural networks" Paris Perdikaris - University of Penns
From playlist Machine Learning for Physics and the Physics of Learning 2019
Neural Networks Pt. 2: Backpropagation Main Ideas
Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand. NOTE: This StatQuest assumes that you
From playlist StatQuest
The StatQuest Introduction to PyTorch
PyTorch is one of the most popular tools for making Neural Networks. This StatQuest walks you through a simple example of how to use PyTorch one step at a time. By the end of this StatQuest, you'll know how to create a new neural network from scratch, make predictions and graph the output,
From playlist StatQuest
This video explains AlphaGo Zero! AlphaGo Zero uses less prior information about Go than AlphaGo. Whereas AlphaGo is initialized by supervised learning on human experts mappings from state to action; AlphaGo Zero is trained from scratch through self-play. AlphaGo Zero achieves this by comb
From playlist Game Playing AI: From AlphaGo to MuZero
Neural Networks Part 6: Cross Entropy
When a Neural Network is used for classification, we usually evaluate how well it fits the data with Cross Entropy. This StatQuest gives you and overview of how to calculate Cross Entropy and Total Cross Entropy. NOTE: This StatQuest assumes that you are already familiar with... The main
From playlist StatQuest
The Evolution of AlphaGo to MuZero
This video covers the developments progression from AlphaGo to AlphaGo Zero to AlphaZero, and the latest algorithm, MuZero. These algorithms from the DeepMind team have gone from superhuman Go performance up to 57 different Atari games. Hopefully this video helps explain how these are rela
From playlist Game Playing AI: From AlphaGo to MuZero
Understanding and Visualizing ResNets that Forever Revolutionized Deep Learning
In December 2015, a published paper rocked the deep learning world. This paper is widely regarded as one of the most influential papers in modern deep learning and has been cited over 110,000 times. The name of this paper was Deep Residual Learning for Image Recognition (aka, the ResNet pa
From playlist Fundamentals of Machine Learning
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
The main ideas behind Backpropagation are super simple, but there are tons of details when it comes time to implementing it. This video shows how to optimize three parameters in a Neural Network simultaneously and introduces some Fancy Notation. NOTE: This StatQuest assumes that you alrea
From playlist StatQuest
Lecture 7E : Long term short term memory
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 7E : Long term short term memory
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]