Skip to content MetaSD Don't just do something, stand there! Reflections on the counterintuitive behavior of complex systems, seen through the eyes of System Dynamics, Systems Thinking and simulation. Menu Noon Networks My browser tabs are filling up with lots of cool articles on networks, which I’ve only had time to read superficially. So, dear reader, I’m passing the problem on to you: Multiscale analysis of Medical Errors Insights into Population Health Management Through Disease Diagnoses Networks

Why Nonlinear Models # Consider a scalar target variable $Y\in\mathbb{R}$ and two independent dummy features $$X=(X_1,X_2)\in\{0,1\}^2.$$Suppose that $$\mathbb{P}(X_j=1)=\mathbb{P}(X_j=0)=0.5,~j\in\{1,2\},$$ and the true regression function equals to the Exclusive Or (XOR) function given by $$\mu(x)=\mathbf{1}[x_1\neq x_2].$$ However, we do not know this population regression function but restrict ourselves to the linear models for convenience. In other words, we only consider the predition rule $f$ from th

ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub Transfer Learning for Named-Entity Recognition with Neural Networks Ji Young Lee , Franck Dernoncourt , Peter Szolovits Correct Metadata for Use this form to create a GitHub issue with struct

Low-Rank Factorization is a powerful technique that compresses neural networks by breaking down large weight matrices into simpler, smaller components, reducing computational demands without sacrificing performance. Perfect for optimizing large language models, these methods streamline model size and speed up inference, making them ideal for real-world deployment

https://gigadom.in/2019/01/10/my-presentations-on-elements-of-neural-networks-deep-learning-part123/ My presentations on 'Elements of Neural Networks & Deep Learning' -Part1,2,3

Standard Neural Networks can learn mathematical operations, but they do not extrapolate. Extrapolation means that the model can apply to larger numbers, well beyond those observed during training. Recent architectures tackle arithmetic operations and can extrapolate; however, the equally important problem of quantitative reasoning remains unaddressed. In this work, we propose a novel architectural element, the Neural Status Register (NSR), for quantitative reasoning over numbers. Our NSR relaxes the discret

Explainable Artificial Intelligence, or XAI for short, is a set of tools that helps us understand and interpret complicated “black box” machine and deep learning models and their predictions. In my previous post I showed you a sneak peek of my newest p...

Maxout Networks introduces a neural network unit designed to enhance performance when trained with dropout by computing the maximum of k affine functions. This architecture achieved new

In the previous parts of this guide, we established a few important ideas:

neural network - If an actor's lip movements don't match the spoken text in a dubbed movie, it not only stresses people who are hard of hearing, b