Now let’s begin the main interest of this blog which is how to interpret different components of LIME. First let’s see what is the final output of the LIME interpretation for a particular data instance. Then we will go deep dive into the different components of LIME in a step by step...
and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been since integrated into DeepLIFT. TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary support for...
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). - jalammar/ecco
use some of the many utilities discussed above Catalyst Philosophy The idea behind the Catalyst is quite simple: collect all the technical, dev-heavy, Deep Learning stuff in a framework, make it easy to re-use boring day-to-day components, focus on research and hypothesis ...
(FFT)), then broken into a series of overlapping chunks calledacoustic frames, each one typically lasting 1/25 to 1/50 of a second. These are digitally processed in various ways and analyzed to find the components of speech they contain. Assuming we've separated the utterance into words, ...
The API reference and the architecture page explain Ecco's components and how they work together. Gallery & Examples Predicted Tokens: View the model's prediction for the next token (with probability scores). See how the predictions evolved through the model's layers. [Notebook] [Colab] Rankin...
and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been since integrated into DeepLIFT. TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary support for...
and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been since integrated into DeepLIFT. TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary support for...
The implementation here differs from the original DeepLIFT by using a distribution of background samples instead of a single reference value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been ...
The implementation here differs from the original DeepLIFT by using a distribution of background samples instead of a single reference value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been ...