The Principles of Diffusion Models: From Origins to Advances pdf: https://
arxiv.org/abs/2510.21890
Tweet:
@jeande_d
-
The Principles of Diffusion Models: From Origins to Advances
By
–
-
Comprehensive Guide to Diffusion Models: Principles and Advances
By
–
The Principles of Diffusion Models: From Origins to Advances By far, the best write-up on diffusion models. This is the go-to doc for foundational principles and essential concepts in diffusion models. 470 pages, 6 chapters of high-quality tokens.
-
Concise MLOps Guide and Complete Machine Learning Package
By
–
I wrote a concise guide to MLOps sometime back(a supplement to complete machine learning package). MLOps: https://
github.com/Nyandwi/machin
e_learning_complete/blob/main/010_mlops/1_mlops_guide.md
…
ML Complete Package: https://
nyandwi.com/machine_learni
ng_complete/
… -
Deep Generative Models: Complete Lecture Resources and Materials
By
–
Deep Generative Models Lectures: videos, slides, notes, papers
-
Deep Generative Modelling: Comprehensive Lectures on Models and Applications
By
–
Great lectures on deep generative modelling, covering a whole bunch of topics, model families, and learning algorithms in generative models space. And applications in vision and natural language processing, and reinforcement learning.
-
Nostalgia for Activation Function Variations in Neural Networks
By
–
Brings back old days of ReLU, MELU, SELU, ELU, GELU, LeakyReLU, PELU, thousands more variations.
-
PyTorch’s Unexpected Growth While Preserving Core Values
By
–
. @PyTorch has snowballed into something never imagined, while still keeping our core values intact!
feels incredible.
-with @ezyang and @apaszke -
Data Mixing Balance: Vision-Language Skills and Regional Imbalance
By
–
That's a great question and will take the opportunity to dash off a bit on the data mixing. Mixing data is a tricky balance it turns out. There were two main factors at play: – we wanted to keep general vision-language skills.
– and had unbalanced regions and languages: think -
Training multilingual cultural model improves multimodal benchmark performance
By
–
Thank you so much and appreciate you taking time to check the paper. We trained a model(
https://
huggingface.co/neulab/Cultura
lPangea-7B
…) on the subset of the dataset and were able to improve performance on many multilingual/cultural multimodal benchmarks. Yeahhh, would be great too if you could also -
Scaling 2.8M Images and 3M Entities with Flexible Data Curation Pipeline
By
–
We have around 2.8M unique images and 3M unique entities and the data curation pipeline allow scaling that up(more images per entity, adding new countries/languages, questions per entity, etc…). Very glad to hear people were asking about this 🙂