Search Results for "data analysis and visualizing"

BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the...

NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI...

Official implementation of paper Deep Feature Rotation for Multimodal Image Style Transfer [NICS'21] We propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while still achieving effective stylization compared to more complex methods in style transfer. Our approach is a representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. Prepare your content image and style...

...Adapt existing networks to your imaging data. Quickly build new solutions to your own image analysis problems. NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use.

...It documents that runs are non-deterministic due to certain GPU operations and reports a median accuracy over multiple trials that is slightly below the single-run result in the paper, reflecting expected variance in practice. The project ships lightweight training, data, and analysis scripts, keeping the footprint small while making the experimental pipeline transparent. It is provided as archived, research-grade code intended for replication and study rather than continuous development.