Hacker News with Generative AI: Neural Networks

Get Started with Neural Rendering Using Nvidia RTX Kit (Vulkan) (nvidia.com)
Neural rendering is the next era of computer graphics.  By integrating neural networks into the rendering process, we can take dramatic leaps forward in performance, image quality, and interactivity to deliver new levels of immersion.
Computer Graphics, Neural Networks, Vulkan, Nvidia, Rendering
29 points by pjmlp 2 days ago | 0 comments
Analogies Between Startups and Neural Networks (mikealche.com)
I’m consulting for a company that the had a crucial team member taking “extended vacations”. Ouch!
Startups, Neural Networks, Business
10 points by yoouareperfect 9 days ago | 0 comments
Computer Simulation of Neural Networks Using Spreadsheets (2018) (arxiv.org)
The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment.
Neural Networks, Computer Simulation, Spreadsheets
40 points by benbreen 11 days ago | 1 comments
Z-Ant: An Open-Source SDK for Neural Network Deployment on Microprocessors (github.com/ZIGTinyBook)
Zant (Zig-Ant) is an open-source SDK designed to simplify deploying Neural Networks (NN) on microprocessors.
Open Source, Neural Networks, Microprocessors, Software Development
10 points by jedisct1 18 days ago | 0 comments
AI Designed Computer Chips That the Human Mind Can't Understand (popularmechanics.com)
A new neural network process has designed wireless chips that can outperform existing ones.
Artificial Intelligence, Computer Hardware, Neural Networks
4 points by givinguflac 33 days ago | 2 comments
An overview of gradient descent optimization algorithms (2016) (ruder.io)
Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks.
Machine Learning, Optimization Algorithms, Neural Networks
135 points by skidrow 43 days ago | 26 comments
An Introduction to Neural Ordinary Differential Equations [pdf] (diposit.ub.edu)
Neural Networks, Machine Learning, Differential Equations, Mathematics
79 points by gballan 56 days ago | 10 comments
NNCP: Lossless Data Compression with Neural Networks (bellard.org)
NNCP is an experiment to build a practical lossless data compressor with neural networks.
Neural Networks, Data Compression, Machine Learning
10 points by ksec 66 days ago | 0 comments
The Structure of Neural Embeddings (seanpedersen.github.io)
A small collection of insights on the structure of embeddings (latent spaces) produced by deep neural networks.
Machine Learning, Deep Learning, Neural Networks, Embeddings
74 points by sean_pedersen 69 days ago | 4 comments
A Gentle Introduction to Graph Neural Networks (2021) (distill.pub)
Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.
Neural Networks, Machine Learning, Graph Theory
362 points by misonic 77 days ago | 33 comments
No More Adam: Learning Rate Scaling at Initialization Is All You Need (arxiv.org)
In this work, we question the necessity of adaptive gradient methods for training deep neural networks.
Machine Learning, Deep Learning, Optimization, Neural Networks
91 points by jinqueeny 79 days ago | 28 comments
Sequence to sequence learning with neural networks: what a decade (youtube.com)
Deep Learning, Neural Networks, Artificial Intelligence, Machine Learning
89 points by dspoka 83 days ago | 31 comments
Neuroevolution of augmenting topologies (NEAT algorithm) (wikipedia.org)
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin.
Artificial Intelligence, Machine Learning, Evolutionary Algorithms, Neural Networks, Genetics
91 points by elsewhen 95 days ago | 33 comments
Inferring neural activity before plasticity for learning beyond backpropagation (nature.com)
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as ‘credit assignment’.
Neural Networks, Machine Learning, Artificial Intelligence, Science
145 points by warkanlock 99 days ago | 49 comments
Quark: Real-Time, High-Resolution, and General Neural View Synthesis (quark-3d.github.io)
We present a novel neural algorithm for performing high-quality, high-resolution, real-time novel view synthesis.
Artificial Intelligence, Computer Vision, 3D Graphics, Neural Networks
27 points by lnyan 100 days ago | 1 comments
Bayesian Neural Networks (cs.toronto.edu)
Bayesian inference allows us to learn a probability distribution over possible neural networks. We can approximately solve inference with a simple modification to standard neural network tools. The resulting algorithm mitigates overfitting, enables learning from small datasets, and tells us how uncertain our predictions are.
Machine Learning, Neural Networks, Bayesian Inference
259 points by reqo 108 days ago | 54 comments
Neural Optical Flow for PIV in Fluids (synthical.com)
Neural Networks, Fluid Dynamics, Computer Vision
15 points by mixeden 118 days ago | 8 comments
Physics-informed Shadowgraph Network: End-to-end Density Field Reconstruction (arxiv.org)
This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks
Physics, Machine Learning, Image Processing, Neural Networks
19 points by mixeden 119 days ago | 1 comments
SharpNEAT – evolving NN topologies and weights with a genetic algorithm (sourceforge.io)
Neuroevolution of Augmenting Topologies (NEAT) is an evolutionary algorithm for evolving artificial neural networks.
Artificial Intelligence, Machine Learning, Genetic Algorithms, Neural Networks
8 points by truegoric 123 days ago | 2 comments
It all started with a perceptron (medium.com)
In homage to John Hopfield and Geoffrey Hilton, Nobel Prize winners for their “fundamental discoveries and inventions that made machine learning and artificial neural networks possible,” I propose to explore the foundations of connectionist AI.
Artificial Intelligence, Machine Learning, History, Neural Networks
30 points by thunderbong 130 days ago | 7 comments
Implementing neural networks on the "3 cent" 8-bit microcontroller (wordpress.com)
Bouyed by the surprisingly good performance of neural networks with quantization aware training on the CH32V003, I wondered how far this can be pushed. How much can we compress a neural network while still achieving good test accuracy on the MNIST dataset?
Neural Networks, Machine Learning, Microcontrollers, Embedded Systems, Image Recognition
159 points by cpldcpu 138 days ago | 20 comments
Neural Networks (MNIST inference) on the "3-cent" Microcontroller (wordpress.com)
Bouyed by the surprisingly good performance of neural networks with quantization aware training on the CH32V003, I wondered how far this can be pushed. How much can we compress a neural network while still achieving good test accuracy on the MNIST dataset?
Machine Learning, Embedded Systems, Hardware, Neural Networks
47 points by Teever 140 days ago | 0 comments
New Algorithm Enables Neural Networks to Learn Continuously (caltech.edu)
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience "catastrophic forgetting" when taught additional tasks: They can successfully learn the new assignments, but "forget" how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
Machine Learning, Artificial Intelligence, Neural Networks
10 points by mindcrime 142 days ago | 0 comments
Solving mazes with neural cellular automata (2021) (umu1729.github.io)
Interactive demonstration of Neural Cellular Maze Solver. This cellular automaton is trained to output the shortest path between the two endpoints. You can interactively edit the maze input by clicking or tapping with selected maze cell types (Wall, Road, Endpoint). The state of each cell is stochastically updated depending on the state of each cell and the four-surrounding cells.
Artificial Intelligence, Neural Networks, Cellular Automata, Mazes, Computer Science
37 points by lnyan 153 days ago | 5 comments
Implementing Neural Networks on the tiniest "3 cent" 8-bit Microcontroller (wordpress.com)
Bouyed by the surprisingly good performance of neural networks with quantization aware training on the CH32V003, I wondered how far this can be pushed. How much can we compress a neural network while still achieving good test accuracy on the MNIST dataset?
Machine Learning, Embedded Systems, Microcontrollers, Neural Networks, Computer Vision
4 points by cpldcpu 159 days ago | 0 comments
Interview with Inventor of Neural Nets Warren McCulloch () [video] (youtube.com)
Artificial Intelligence, History, Interviews, Neural Networks
41 points by ofou 171 days ago | 4 comments
Kolmogorov-Arnold networks may make neural networks more understandable (quantamagazine.org)
By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.
Neural Networks, Artificial Intelligence, Mathematics, Scientific Discovery
282 points by isaacfrond 176 days ago | 78 comments
Real-Time Neural Appearance Models [video] (youtube.com)
Neural Networks, Computer Vision, Video, Real-time Applications
5 points by cyanf 177 days ago | 0 comments
Neural Networks: Zero to Hero (karpathy.ai)
Neural Networks, Machine Learning, Programming, Tutorials
4 points by cube2222 192 days ago | 0 comments
Ask HN: What are some "toy" projects you used to learn neural networks hands-on? (ycombinator.com)
Neural Networks, Machine Learning, Programming, Learning Resources, Software Development
76 points by profsummergig 206 days ago | 48 comments