How to Encode Constraints to the Output of Neural Networks | by Runzhong Wang | Apr, 2024
A summary of available approachesImage generated by ChatGPT based on this article’s content.Neural networks are indeed powerful. However, as the ...
Tag: neural
VoiceCraft: A transformer-based Neural Codec Language Model (NCLM) that achieves cutting-edge performance in speech editing and zero-shot TTS
When textless natural language processing (NLP) initially emerged, the main concept involved training a language model on sequences of discrete ...
OA-CNN: A family of networks that integrates a lightweight module to greatly improve the adaptability of sparse convolutional neural networks (CNN) at minimal computational cost
In the realm of 3D scene understanding, a major challenge arises due to the irregular and sparse nature of 3D ...
GeFF: Revolutionizing robot perception and action with scene-level generalizable neural feature fields
When a buzzing noise catches your attention, you're walking down the bustling city street, carefully holding your morning coffee. Suddenly, ...
Quantum Machine Learning with Python: Kernel Methods and Neural Networks | by Xavier Vasques | Mar, 2024
Photo by Annamária Borsos (used with permission)Quantum Machine Learning (QML) represents a fascinating convergence of quantum computing and machine learning ...
Revolutionizing robotic surgery with neural networks: Overcoming catastrophic forgetting through privacy-preserving continuous learning in semantic segmentation
Deep neural networks (DNN) stand out for improving surgical precision through semantic segmentation and accurate identification of robotic instruments and ...
Revolutionizing multivariate long-term time series forecasting: Introducing PDETime, a novel machine learning approach that leverages PDE neural solvers to achieve unparalleled accuracy
Multivariate time series forecasting is the cornerstone of countless applications, ranging from weather prediction to energy consumption management in today's ...
Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton
This guest post is written by Vihan Lakshman, Tharun Medini, and Anshumali Shrivastava from ThirdAI. Large-scale deep learning has recently ...
This AI article presents mixed-precision training for Fourier neural operators: uniting efficiency and accuracy in high-resolution PDE solutions
Neural operators, specifically Fourier neural operators (FNO), have revolutionized the way researchers approach solving partial differential equations (PDEs), a fundamental ...
This AI article introduces PirateNets: a novel AI system designed to facilitate stable and efficient training of neural network models based on deep physics
As the world of computational science continually evolves, physics-based neural networks (PINN) stand out as an innovative approach to address ...
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