Categories: FAANG

Exploring Empty Spaces: Human-in-the-Loop Data Augmentation

Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these “unknown unknowns” is a time- and creativity-intensive task. In this work, we introduce Amplio, an interactive tool to help practitioners navigate “unknown unknowns” in unstructured text datasets and improve data diversity by systematically identifying empty data spaces to explore. Amplio…
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Some recent Chroma renders

Model: https://huggingface.co/silveroxides/Chroma-GGUF/blob/main/chroma-unlocked-v38-detail-calibrated/chroma-unlocked-v38-detail-calibrated-Q8_0.gguf Workflow: https://huggingface.co/lodestones/Chroma/resolve/main/simple_workflow.json Prompts used: High detail photo showing an abandoned Renaissance painter’s studio…

37 mins ago

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices •…

37 mins ago

Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training

Pandas DataFrames are powerful and versatile data manipulation and analysis tools.

37 mins ago

Securing America’s Defense Industrial Base

Palantir FedStart and the Path to CMMC ComplianceSecuring the Defense Industrial BaseNever has the imperative…

37 mins ago

No-code data preparation for time series forecasting using Amazon SageMaker Canvas

Time series forecasting helps businesses predict future trends based on historical data patterns, whether it’s…

38 mins ago

Beyond static AI: MIT’s new framework lets models teach themselves

MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and…

2 hours ago