William Merrill, a graduate student at NYU, recently co-authored a study that used computational complexity theory to quantify the strength of “chain-of-thought” reasoning in neural networks. https://lnkd.in/edQVKiQE
Quanta Magazine’s Post
More Relevant Posts
-
Join us Wednesday, July 24 for a joint TILOS-HDSI seminar with Robert Nowak of the University of Wisconsin: What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications. https://lnkd.in/dACuUJp6
To view or add a comment, sign in
-
AI & Reinforcement Learning Researcher | Asst. Professor | NVidia DLI Ambassador | GitHub Campus Advisor | Cyclist 🚴♂️
Important terms in Artificial Neural Networks: 1. Neuron: The basic computational unit of a neural network, inspired by biological neurons. 2. Activation Function: A function applied to the output of each neuron, determining its output or "firing" behavior. 3. Weights: Parameters in the network that adjust the strength of connections between neurons during training. 4. Bias: An additional parameter added to each neuron, providing flexibility and helping the network learn complex patterns. 5. Layers: Neurons are organized into layers (input, hidden, output), forming the structure of the network.
To view or add a comment, sign in
-
Visualization of the excitability of neurons in an artificial neural network. Different from traditional artificial neural networks based on multiplication and addition operations.
To view or add a comment, sign in
-
Day-7,8 and 9 of the ML/AI 10-DAY Challenge :- Took me a good 3 days to complete the Entire or Not-so-Entire theory about the Neural networks.. Biological motivation Artificial neurons Activation function Perceptron Artificial neural networks Characteristics of an ANN Backpropagation Along with the Mathematics behind it.
To view or add a comment, sign in
-
💡 Exploring Computer Vision: Question 4 of 100 🤔 How do activation functions in Convolutional Neural Networks (CNNs) operate, and could you provide examples of popular activation functions used in CNNs? Share your insights in the comments below! #ComputerVision #CNNs #ActivationFunctions 🧠🖥️
To view or add a comment, sign in
-
Class* is now in session 🏫 👩🏫 *Multi-Class Quantum Convolutional Neural Networks (QCNN) that is 😄 Marco Mordacci, Davide Ferrari and Michele Amoretti from Università degli Studi di Parma have shared code from their QCNN paper 🙌 Implement multi-class QCNN and outperform classical NNs with their PL community demo👇 https://lnkd.in/g7PSn9G4 / This code supports their recent research paper "Multi-Class Quantum Convolutional Neural Networks” View it here: https://lnkd.in/gRhXjZsW Have code you think the quantum community would benefit from? Why not share it? Learn more here: https://lnkd.in/gr2AWupn
To view or add a comment, sign in
-
-
An artificial neural network (ANN) is a machine learning model inspired by the structure and function of the human brain. One of the basic components of Nanoinformatics
To view or add a comment, sign in
-
#FlashbackFriday in 1943: "A Logical Calculus of the Ideas Immanent in Nervous Activity" Two scientists, Warren S. McCulloch and Walter H. Pitts, publish the groundbreaking paper 'A Logical Calculus of the Ideas Immanent in Nervous Activity'. The paper quickly became a foundational work in the study of artificial neural networks and has many applications in artificial intelligence research. In it McCulloch and Pitts described a simplified neural network architecture for intelligence, and while the neurons they described were greatly simplified compared to biological neurons, the model they proposed was enhanced and improved upon by subsequent generations of researchers. #computerscience #neuralnetwork #techhistory #2jdata
To view or add a comment, sign in
-
-
Co-Founder, Chief AI & Analytics Advisor @ InstaDataHelp | Innovator and Patent-Holder in Gen AI and LLM | Data Science Thought Leader and Blogger | FRSS(UK) FSASS FRIOASD | 16+ Years of Excellence
Understanding the Science Behind Weight Initialization in Neural Networks Exciting news! I've just published a new blog post that explores the science behind weight initialization in neural networks. 🧠💡 Proper weight initialization is crucial for achieving optimal performance and faster convergence during the training process. In this article, we dive into the impact of weight initialization on network stability, as well as common techniques like random initialization, Xavier initialization, and He initialization. If you're interested in understanding the intricacies of weight initialization, check out the full blog post here: [link](https://ift.tt/18rKfAy) #neuralnetworks #weightinitialization #deeplearning https://ift.tt/18rKfAy
To view or add a comment, sign in