Eleuther AI introduces new machine learning framework to analyze neural network training via Jacobian matrix
Neural networks have become fundamental tools in computer vision, NLP, and many other fields, offering capabilities to model and predict ...
Tag: neural
Latent Functional Maps: A Robust Machine Learning Framework for Analyzing Neural Network Representations
Neural networks (NN) remarkably transform high-dimensional data into compact, lower-dimensional latent spaces. While researchers traditionally focus on model outputs such ...
Neural Information Processing Systems (NeurIPS) 2024
Apple is presenting new research at the annual conference on Neural Information Processing Systems (NeurIPS), which takes place in person ...
ZipNN: a new lossless compression method adapted to neural networks
The rapid advancement of large language models (LLMs) has exposed critical infrastructure challenges in model implementation and communication. As models ...
Bridging neural dynamics and collective intelligence: A study on adaptive multi-agent systems for effective consensus building in complex and dynamic environments
The study of collective decision making in biological and artificial systems addresses critical challenges to understanding how groups achieve consensus ...
This Amazon AI paper introduces DF-GNN: a dynamic kernel fusion framework for accelerating attention graph neural networks on GPUs
Graph neural networks (GNN) is a rapidly advancing field in machine learning, specifically designed to analyze graphically structured data representing ...
Geometric Distributions: Advances in 3D Neural Surface Modeling with Diffusion Models
Geometric representations play a crucial role in solving complex 3D vision problems. The rapid evolution of deep learning has sparked ...
Neural Magic Releases 2:4 Sparse Llama 3.1 8B: Smaller Models for Efficient GPU Inference
The rapid growth in the size of ai models has brought with it significant computational and environmental challenges. Deep learning ...
DeBaTeR: A new artificial intelligence method that leverages temporal information in collaborative neural graph filtering to improve both denoising and prediction performance.
Recommender systems have been widely applied to study user preferences; However, they face significant challenges in accurately capturing user preferences, ...
NeuralDEM: Pioneering high-performance simulation of large-scale particle systems with multi-branch neural operator architectures
Advances in particle flow simulation have had a significant impact on industries ranging from mining to pharmaceuticals. Particle systems consist ...
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