Video recording of our webinar about dstack and reproducible ML workflows, AVL binary tree operations, Ultralytics YOLOv8, training XGBoost, productionize ML models, introduction to forecasting ensembles, domain expansion of image generators, Muse, X-Decoder, Box2Mask, RoDynRF, AgileAvatar and more.
VIDEO
dstack — a command-line utility to provision infrastructure for ML workflows
Video recording of our webinar about dstack and reproducible ML workflows by Andrey Cheptsov.
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ARTICLES
AVL Binary Tree Operations
In this article, the author described AVL trees and operations you can perform on them, such as inserting a node in different variants (for example, left, right or right, right).
Ultralytics YOLOv8: State-of-the-Art YOLO Models
YOLOv8 is the latest family of YOLO based Object Detection models from Ultralytics providing state-of-the-art performance. This article looks into the latest improvements and features added to YOLOv8, and provides a guide on using it in practice.
End-to-End MLOps with Snowpark Python and MLFlow
How would you leverage Snowpark Python for operationalizing your machine learning models within the flow of your existing MLOps processes? The article provides detailed answers and looks into end-to-end MLOps, from A to Z.
Building a Predictive Maintenance Solution Using AWS AutoML and No-Code Tools
Industrial machine, equipment, and vehicle operators need to reduce maintenance costs while operating under strict constraints. This article presents a predictive maintenance solution built using AutoML and no-code tools powered by AWS. Check it out!
PAPERS & PROJECTS
Zero-Shot Text-Guided Object Generation with Dream Fields
Dream Fields can generate the geometry and color of a wide range of objects without 3D supervision. It combines neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Take a look!
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging
AgileAvatar is a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters. To ensure the discrete parameters are optimized, a cascaded relaxation-and-search pipeline is implemented.
RoDynRF: Robust Dynamic Radiance Fields
In this work, the authors address the robustness issue of dynamic radiance field reconstruction methods by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). Learn how they do it!
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution
Box2Mask is a novel single-shot instance segmentation approach, which integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision. Check the paper out!
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