Data Phoenix Digest - ISSUE 4.2023

Data Phoenix Digest - ISSUE 4.2023

NEWS


Share your latest news and advancements in Data, AI, and Machine Learning with us, and let us help you showcase your solutions to a broader audience.


VIDEO

Deploying DL models with Kubernetes and Kubeflow.


Video recording of our webinar about deploying ML models by Alexey Grigorev .


If you have interesting topics or projects that you would like to share with the world in our webinars, you can submit them?here.



ARTICLES

A Guide to the YOLO Family of Computer Vision Models

YOLO (You Only Look Once) is an advanced algorithm that can detect and recognize various objects in a picture in real-time, faster and more accurately than any other algorithm. The YOLO family is advancing, step-by-step, and every update adds to its potential. Learn more about it!

Discover the 4 Magical Methods to Detect AI-Generated Text (including ChatGPT)

The recent launch of OpenAI’s GPT-3 and ChatGPT has sparked a revolution in the field of AI & NLP. However, it has also raised justified concerns about the potential misuse of text generated by AI. Here are several methods for detecting AI-generated text.

End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face

The SensorFlow team unveils the crucial details of building an end-to-end ML pipeline for Semantic Segmentation tasks with TFX and various Google Cloud services, such as Dataflow, Vertex Pipelines, Vertex Training, and Vertex Endpoint. Check it out!


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PAPERS & PROJECTS

EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets

EPiC-GAN is an equivariant point cloud generative adversarial network that can produce point clouds of variable multiplicity. EPiC-GAN can scale well to large particle multiplicities and achieve high generation fidelity on benchmark jet generation tasks. Learn more!

Behind the Scenes: Density Fields for Single View Reconstruction

In this paper, the authors introduce a neural network that predicts an implicit density field from a single image. It maps every location in the frustum of the image to volumetric density. Our network can be trained through self-supervision from only video data. Take a look!

Tracr: Compiled Transformers as a Laboratory for Interpretability

Tracr is a "compiler" for translating human-readable programs into weights of a transformer model. It can be used to create ground truth transformers that implement programs, including computing token frequencies, sorting, and Dyck-n parenthesis checking.


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