#SyntheticDataGeneration

2025-05-12

Utility AI deployments show climate-specific pattern evolution, with Meralco's storm-resilient vision systems and PG&E's wildfire predictors establishing dual benchmarks for tropical vs. ❤️

redrobot.online/2025/05/region

Verified by MonsterInsights
iCode2Ifeanyi5
2024-11-15

I scaled up the popular Palmer Penguins machine learning dataset from 344 rows to 100k rows using adversarial random forest, with an accuracy of 88%.

Now, you have more rows of data with which to train your classification models.

You can download it here, along with R & Python scripts, to load and view the dataset: ieee-dataport.org/documents/pa

Have a dataset you want to scale up? Say hello!

InstructLabInstructLab
2024-09-16

"How InstructLab’s synthetic data generation enhances " - Cedric Clyburn and Legare Kerrison put together this article on 's synthetic data generation process to help break down how it works. Take a look!

redhat.com/en/blog/how-instruc

Anita Graser 🇪🇺🇺🇦🇬🇪underdarkGIS@fosstodon.org
2023-03-28

It's been a pleasure to present our #DeepLearning from Trajectory Data review paper at #BMDA2023

datastories.org/bmda23/BMDA23P

Our review covers 8 use cases:
1. #LocationClassification
2. #ArrivalTimePrediction
3. #TrafficFlow #prediction
4. #Trajectory prediction
5. Trajectory #classification
6. Next location prediction
7. #AnomalyDetection
8. #SyntheticDataGeneration

The price for most surprising approach 🏆 goes to natural language #NLP #Transfomers for #Traffic volume prediction

#EDBT2023

Screenshot of the intro slide of my talk titled: 

Deep Learning From Trajectory Data - A Review of Deep Neural Networks and the Trajectory Data Representations to Train Them 

presented at the Big Mobility Data Analytics (BMDA) workshop at EDBT/ICDT Conference
on 2023-03-28

authored by 
Anita Graser, Anahid Jalali, Jasmin Lampert, Axel Weißenfeld and Krzysztof Janowicz

This work is mainly funded by the EU’s Horizon Europe research and innovation program under Grant No. 101070279 MobiSpaces and No. 101021797 STARLIGHT.Slide on the traffic volume prediction use case, highlighting a paper using NLP Transformers

Client Info

Server: https://mastodon.social
Version: 2025.04
Repository: https://github.com/cyevgeniy/lmst