Through this report, we explore text style transfer through an applied use case – neutralizing subjectivity bias in text. @FastForwardLabs#DataScience#NLP https://t.co/4MbD6TGKZp
I'll be speaking at #ODSCWest tomorrow (11/2) on our recent research, FF24: Text Style Transfer. Come say hello if you're at the conference! @cloudera@FastForwardLabs
My first @huggingface space!
A @streamlit app that demonstrates how the #NLP task of Text Style Transfer can be applied to enhance the human writing experience.
Suggest text re-writes between styles like:
• Subjective to neutral
• Informal to formal
https://t.co/bvXyt1dinV
Excited to share that Part 3 of the @FastForwardLabs technical blog series on Text Style Transfer is out!
In this post, we explore and implement task-specific metrics for evaluating two criteria: style transfer strength & content preservation.
https://t.co/mlLdMFKs0y
We've been exploring the NLP task of Text Style Transfer here @FastForwardLabs.
Check out our recent blog post where we apply @huggingface transformers to neutralize subjectivity bias in text.
Stay tuned for a Part 2 continuation of this work!
https://t.co/AZfKpo7mh1
Wondering how to achieve continuous model monitoring on Cloudera Machine Learning (CML)?
Checkout our newly released Applied ML Prototype by @andrewrreed that shows you how using @EvidentlyAI's drift monitoring reports! #MLOps
https://t.co/kQOoHRoe3U
Building on our previous blog on Video Understanding, we're pleased to bring you Why and How Convolutions Work for Video Classification! Take a deep dive through space and time with us: https://t.co/LBXPT1xta6
Check out our newest blog post, An Introduction to Video Understanding: Capabilities and Applications. In it we explore ML tasks associated with this field, as well some of their real world applications.
You can find it here https://t.co/SpUADyenuL
Check out our newest blog post, The Rise of Unstructured Data. In it we explore how much data there is in the world, what types there are, and what that means for AI and businesses.
You can find it here
https://t.co/hDwB7k5yVZ
In case you missed this one: a very thorough review by @FastForwardLabs on how to infer concept drift when labeled data is not readily accessible 👇🏼
https://t.co/u2coH4ZBUi
New research alert!
Our own @andrew_reed_r and @NishaMuktewar have been exploring the issue of detecting concept drift when the cost of obtaining labeled data to validate model performance is high.
Check out our latest report here: https://t.co/gm1kKzf1LZ
Latest research cycle has dropped! Melanie and Chris spent the past couple of months exploring multi-objective hyperparameter optimization. Here's our write up, with links to some tutorial and experiment notebooks.
https://t.co/udabNaCM4x
#Deeplearning for Offline Signature Verification.
Interested in how the task of signature verification (a common use case in the banking/finance sector) can be addressed using #MachineLearning ?
@andrew_reed_r and I wrote an introductory post https://t.co/nuPxb0I5N0
Our short report covers all of this, and addresses many of the considerations necessary for building thoughtful, information-driven session-based recommender systems. Comes with code to play with!