Sushant Joshi

NiNo: A Novel Machine Learning Approach to Accelerate Neural Network Training through Neuron Interaction and Nowcasting Researchers from Samsung’s SAIT AI Lab, Concordia University, Université de Montréal, and Mila have introduced a novel approach known as Neuron Interaction and Nowcasting (NINO) networks. This method aims to significantly reduce training time by predicting the future state of network parameters. Rather than applying an optimization step at every iteration, as with traditional methods, NINO employs a learnable function to predict future parameter updates periodically. By integrating neural graphs—which capture the relationships and interactions between neurons within layers—NINO can make rare yet highly accurate predictions. This periodic approach reduces the computational load while maintaining accuracy, particularly in complex architectures like transformers. At the core of the NINO methodology lies its ability to leverage neuron connectivity through graph neural networks (GNNs). Traditional optimizers like Adam treat parameter updates independently without considering the interactions between neurons. NINO, however, uses neural graphs to model these interactions, making predictions about future network parameters in a way that reflects the network’s inherent structure. The researchers built on the Weight Nowcaster Networks (WNN) method but improved it by incorporating neuron interaction modeling. They conditioned NINO to predict parameter changes for the near and distant future. This adaptability allows NINO to be applied at different stages of training without requiring constant retraining, making it suitable for various neural architectures, including vision and language tasks. The model can efficiently learn how network parameters evolve by using supervised learning from training trajectories across multiple tasks, enabling faster convergence.... Read our article on this:https://lnkd.in/gSQFYtcG Paper: https://lnkd.in/g9rJ5JT2 GitHub Page: https://lnkd.in/gJPbT-kM

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Build your machine learning portfolio with our Python for Machine Learning: The Essential Starter Kit course, starting September 9. Register before August 12 and receive a 10 per cent early bird discount with code PMLEB10. This code must be applied at the time of registration and cannot be combined with other discounts or promotions. Perfect for professionals looking to advance into roles that include machine learning, this course is designed to introduce you to Python, a programming language known for its simplicity and readability. You will understand how Python can be used for machine learning tasks and learn to use its popular packages and libraries for data preparation. Learn more and register: https://lnkd.in/gz9wS_fp Mehrdad Pirnia Fuat Can Beylunioğlu Manda (Hongxiu) Li, Ph.D. #Python #Machinelearning

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A session on "Julia Compiler Secrets - Static Analysis" by Keno Fischer and Shuhei Kadowaki The Julia compiler has evolved significantly, expanding its static analysis capabilities beyond type inference to include effect analysis, escape analysis, and optimized generics. These advancements, introduced in recent releases like Julia 1.8 and 1.11, are paving the way for faster, more efficient, and safer code. The talk delved into how these new capabilities enhance code performance and correctness, offering insights into the future of the Julia compiler. Exciting times ahead for Julia developers #JuliaCon #JuliaLang #CompilerTech #StaticAnalysis #PerformanceOptimization

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AEs (Variational Autoencoders) and AEs (Autoencoders) are neural network architectures used for dimensionality reduction and data compression. They share a similar structure with an encoder and a decoder, but they differ in how they handle the latent space, which is a compressed representation of the input data. Here is a good article explaining the difference between AEs and VAEs. #AE #VAE #neuralnetworks #deeplearning #computervision I like to share my Data Science journey with my LinkedIn friends… I believe learning everyday is part of life. Follow me if you like my posts. keep reading. keep learning.

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