Stacey Svetlichnaya

My recently favorite reports

A general workflow for exploring LLMs and prompt versions with W&B

Visualize and analyze protein sequences and 3D structures with W&B Tables

How to visualize time series data and analyze relevant models in W&B

This article explains how to diversify and streamline image generation across visual domains using StarGAN v2 and Weights & Biases.

This article explores a range of adversarial examples in multi-agent settings, demonstrating how adversarial policies can win reliably without even playing the game.

How do we distinguish signal from pareidolia (imaginary patterns)? This article is showcases what is possible with W&B and aims to inspire further exploration.

Interactively exploring ML data and predictions in the audio domain with our new Tables feature

Exploring the ecological footprint of 195 countries over 50 years—and how this interacts with the country's happiness and values.

Visualize & precisely annotate 3D scenes in W&B

This article explores semantic segmentation for scene parsing on Berkeley Deep Drive 100K (BDD100K) including how to distinguish people from vehicles.

open accessible and meaningful collaborations on W&B

This article introduces the SafeLife Benchmark Launch with Partnership on AI and explains its importance for the future development of ethical AI.

A benchmark to extract text from visually-structured forms, starting with political ad receipts

This article walks through the process of developing the baseline and exploring submissions to the Drought Watch benchmark

This article includes first runs and observations in the SafeLife benchmark in Weights & Biases, which was developed in collaboration with the Partnership on AI (PAI).

work on text classification, understanding, and generation

A quick story of the challenges and opportunities in reproducing language models

In this article, we look at how to use Hugging Face to explore models for natural language understanding

A quick exploration of the Reddit GoEmotions dataset

A guide on how to log and organize text data and language model predictions with our old friend William Shakespeare

How to use W&B features

How to plot various types of metrics in W&B with custom charts

Log to a custom chart from Python with W&B

How to version and interactively explore data and predictions across train/val/test with W&B's new Tables feature

Showcasing features & applications in W&B

This article introduces Weights & Biases Artifacts, which offer lightweight dataset versioning, and gives an easy-to-follow example so that you can see how they work.

A metareport on custom visualization support in W&B

In this article, we take a look at how to customize charts of trees and graphs in Weights & Biases.

How to version and visualize cloud data for machine learning

This article explains how to easily adapt a Weights & Biases Custom Chart preset for individual, specific use cases.

Set up powerful and flexible analysis across runs logging structured data

How to visualize classification models in a few lines with the W&B Python API

In this article, we take a look at how to log and explore bounding boxes with Weights & Biases

A collection of my Tables reports

A collection of W&B Reports for AV use cases

In this article, we will explore data-parallel distributed training in Keras trying different configurations of GPU count.

In this article, we explore how to build a multi-class confusion matrix in Weights & Biases using Vega, fine-tuning a CNN to predict one of 10 classes of living things.

Chemistry, computer vision & everything without its own category (—for now!)

Unsupervised learning of depth perception from dashboard cameras.

In this article, we take a look at how to train a deep net to extract foreground and background in natural images and videos.

In this article, we look at how to predict chemical properties from molecular structures with random forests and deep nets using W&B.

Exploring chest x-ray data and strategies for real-world long-tailed data

A quick example of graph views for prompt engineering & working with language models

Predicting protein-ligand interactions with deep learning