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Deep learning model integrates dynamic firebreak placement for improved wildfire spread prediction

Abstract. Wildfire management strategies increasingly demand accurate predictive models that integrate real-time intervention measures. Despite advances in machine learning (ML) for wildfire modelling, existing approaches largely overlook the role of fire

Insighttechnologysciencedeep learning

nhess.copernicus.org·Climate Science·6d ago·8 min read

The Annotated JEPA: A Complete Walkthrough of Joint Embedding Predictive Architectures

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elonlit.com6d ago

Logmoid: A new activation function proposed for neural networks

Insighttechnologymachine learning

Shared by @gp_pulipaka ↗

geni.us7d ago

Annotated Historical Timeline of Modern AI and Deep Learning Foundations

Machine learning (ML) is the science of credit assignment. It seeks to find patterns in observations that explain and predict the consequences of events and actions. This then helps to improve future performance. Minsky's so-called "fundamental credit ass

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Shared by @SchmidhuberAI ↗

arxiv.org7d ago

The contested origins of GANs: Schmidhuber's 1990-1991 adversarial neural networks predate the 2014 breakthrough

GANs were first published in 1990 by Juergen Schmidhuber

Insighttechnologysciencedeep learningpeople.idsia.ch·Twitter AI·14d ago·19 min read

N-BEATS deep learning model with mobility data improves COVID-19 case forecasting

COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting ne

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journals.plos.org·Tech by Flipboard·17d ago·59 min read

Understanding Scaling Laws in Deep Learning: A Framework for Optimal Compute Allocation

Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, wh

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lilianweng.github.io·Tech by Flipboard·20d ago·25 min read

Optimal deep learning pipeline for animal activity recognition using bio-logging data

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Shared by @methodsinecoevol.bsky.social ↗

besjournals.onlinelibrary.wiley.com20d ago

Schmidhuber claims 1991 paper introduced first linear-scaling Transformer variant, predating Google's 2017 model

Opiniontechnologyscience

Shared by @SchmidhuberAI ↗

people.idsia.ch21d ago

Moebius: A 0.2B Parameter Lightweight Image Inpainting Framework Matching 10B-Level Performance

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hustvl.github.io24d ago

Optimizing Deep Learning Performance Through First-Principles Reasoning

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horace.io1mo ago

Parameters vs. Computation: Understanding Deep Learning Model Efficiency Metrics

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parl.ai2mo ago

Talos: FPGA-Based Hardware Accelerator for Efficient Convolutional Neural Network Inference

Newstechnologydeep learning

talos.wtf4mo ago

Andrej Karpathy's Course: Building Neural Networks from Scratch to GPT

A course by Andrej Karpathy on building neural networks, from scratch, in code.

educationmachine learningprogrammingkarpathy.ai·Hacker News: Front Page·6mo ago·1 min read

mHC: A Manifold-Constrained Framework to Stabilize and Scale Hyper-Connections in Neural Networks

Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial pe

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arxiv.org6mo ago

Chord Mini: Open-Source Tool for Song Analysis Using Deep Learning

Producttechnologymusic

Product Hunt11mo ago

Comprehensive Guide to AI Fundamentals and Algorithms

Aman's AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.

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aman.ai11mo ago

PyImageSearch4

Deep learning model integrates dynamic firebreak placement for improved wildfire spread prediction

The Annotated JEPA: A Complete Walkthrough of Joint Embedding Predictive Architectures

Logmoid: A new activation function proposed for neural networks

Annotated Historical Timeline of Modern AI and Deep Learning Foundations

The contested origins of GANs: Schmidhuber's 1990-1991 adversarial neural networks predate the 2014 breakthrough

N-BEATS deep learning model with mobility data improves COVID-19 case forecasting

Understanding Scaling Laws in Deep Learning: A Framework for Optimal Compute Allocation

Optimal deep learning pipeline for animal activity recognition using bio-logging data

Schmidhuber claims 1991 paper introduced first linear-scaling Transformer variant, predating Google's 2017 model

Moebius: A 0.2B Parameter Lightweight Image Inpainting Framework Matching 10B-Level Performance

Optimizing Deep Learning Performance Through First-Principles Reasoning

Parameters vs. Computation: Understanding Deep Learning Model Efficiency Metrics

Talos: FPGA-Based Hardware Accelerator for Efficient Convolutional Neural Network Inference

Andrej Karpathy's Course: Building Neural Networks from Scratch to GPT

mHC: A Manifold-Constrained Framework to Stabilize and Scale Hyper-Connections in Neural Networks

Chord Mini: Open-Source Tool for Song Analysis Using Deep Learning

Comprehensive Guide to AI Fundamentals and Algorithms