Data loading is a critical aspect of deep learning workflows, whether you're focused on training or inference. However, it often presents a paradox: the need...
AI Brain Implant Restores Bilingual Communication for Stroke Survivor
Scientists have enabled a stroke survivor, who is unable to speak, to communicate in both Spanish and English by training a neuroprosthesis implant to decode...
Perception Model Training for Autonomous Vehicles with Tensor Parallelism
Due to the adoption of multicamera inputs and deep convolutional backbone networks, the GPU memory footprint for training autonomous driving perception models...
Release: PyTorch Geometric Container for GNNs on NGC
The NVIDIA PyG container, now generally available, packages PyTorch Geometric with accelerations for GNN models, dataloading, and pre-processing using...
Available Now: NVIDIA AI Accelerated DGL and PyG Containers for GNNs
From credit card transactions, social networks, and recommendation systems to transportation networks and protein-protein interactions in biology, graphs are...
Transforming Industrial Defect Detection with NVIDIA TAO and Vision AI Models
Efficiency is paramount in industrial manufacturing, where even minor gains can have significant financial implications. According to the American Society of...
Analyzing the Security of Machine Learning Research Code
The NVIDIA AI Red Team is focused on scaling secure development practices across the data, science, and AI ecosystems. We participate in open-source security...
Research Unveils Breakthrough Deep Learning Tool for Understanding Neural Activity and Movement Control
A primary goal in the field of neuroscience is understanding how the brain controls movement. By improving pose estimation, neurobiologists can more precisely...
Develop Physics-Informed Machine Learning Models with Graph Neural Networks
NVIDIA Modulus is a framework for building, training, and fine-tuning deep learning models for physical systems, otherwise known as physics-informed machine...
Sparsity in INT8: Training Workflow and Best Practices for NVIDIA TensorRT Acceleration
The training stage of deep learning (DL) models consists of learning numerous dense floating-point weight matrices, which results in a massive amount of...
Deep learning models require hundreds of gigabytes of data to generalize well on unseen samples. Data augmentation helps by increasing the variability of...
Increasing Inference Acceleration of KoGPT with NVIDIA FasterTransformer
Transformers are one of the most influential AI model architectures today and are shaping the direction of future AI R&D. First invented as a tool for...
