By arXiv.org -
2020-10-01
Benchmarks such as GLUE have helped drive advances in NLP by incentivizing
the creation of more accurate models. While this leaderboard paradigm has been
remarkably successful, a historical focus on p ...
By arXiv.org -
2020-10-23
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified
text-to-text format and scale to attain state-of-the-art results on a wide
variety of English-language NLP tasks. In this paper, ...
By The Gradient -
2020-11-21
A broad overview of the sub-field of machine learning interpretability; conceptual frameworks, existing research, and future directions.
By Medium -
2021-02-18
In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. In particular, we discussed the promise of model observability & model monitoring…
By Medium -
2021-01-04
A groundbreaking and relatively new discovery upends classical statistics with relevant implications for data science practitioners and…
By Medium -
2020-12-03
This tutorial covers the entire ML process, from data ingestion, pre-processing, model training, hyper-parameter fitting, predicting and storing the model for later use. We will complete all these…
