https://huggingface.co/collections/osanseviero/model-merging-65097893623330a3a51ead66

Model Merging: papers
#Paper

https://github.com/arcee-ai/mergekit
Model Merging: toolkit
#Frameworks

https://arxiv.org/pdf/2404.19756 Kolmogorov-Arnold Networks #paper

https://kindxiaoming.github.io/pykan/ Kolmogorov Arnold Network #framework #library

https://github.com/SeldonIO/alibi-detect Algorithms for outlier, adversarial and drift detection
https://github.com/SeldonIO/alibi Algorithms for explaining machine learning models
#Frameworks #library #anomaly #drift

Grokking:
1. Fist paper: https://arxiv.org/abs/2201.02177
2. Transformers: https://arxiv.org/pdf/2405.15071
3. Simple framework: https://arxiv.org/pdf/2405.20233

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries.
https://mars-project.readthedocs.io/
#Frameworks

Tutorial: Scalable and Distributed ML Workflows with DVC and Ray
Part 1: https://dvc.ai/blog/dvc-ray
Part 2: https://dvc.ai/blog/dvc-ray-part-2

https://web.stanford.edu/~jurafsky/slp3/
#books Speech and Language Processing (3rd ed. draft)
Dan Jurafsky and James H. Martin

RAG #Frameworks
https://github.com/infiniflow/ragflow

NVidia monitoring #Tools :
1. GPUStat https://github.com/wookayin/gpustat
2. Nvtop https://github.com/Syllo/nvtop
3. NVITOP https://github.com/XuehaiPan/nvitop

SOTA in unsupervised semantic segmentation:
1. STEGO: Unsupervised Semantic Segmentation by Distilling Feature Correspondences - 2022 https://arxiv.org/abs/2203.08414
2. HP: Leveraging Hidden Positives for Unsupervised Semantic Segmentation -2023 https://arxiv.org/abs/2303.15014
3. CAUSE: Causal Unsupervised Semantic Segmentation - 2023 https://arxiv.org/abs/2310.07379
#Paper

https://arxiv.org/pdf/2408.04840v1
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
#Paper

https://encord.com/blog/dimentionality-reduction-techniques-machine-learning/
Dimensionality reduction techniques in one place #FYI #Tips

NVidia: Traditional machine learning on GPU: various clustering, UMAP, TSNE, PCA, etc. #FYI #library
https://github.com/rapidsai/cuml
https://docs.rapids.ai/api/cuml/stable/

https://arxiv.org/abs/2410.05258
Differential Transformer #Paper

Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code.
https://numba.pydata.org/ #Frameworks #library

Dino/Dino v2 explained: Self-distillation with no labels & etc. #FYI #Tips #Explained #Tutorial
1. https://medium.com/@anuj.dutt9/emerging-properties-in-self-supervised-vision-transformers-dino-paper-summary-4c7a6ed68161 Original Dino
2. https://encord.com/blog/dinov2-self-supervised-learning-explained/
3. https://www.picsellia.com/post/dinov2-steps-by-steps-explanations-picsellia
4. https://www.ai-bites.net/dino-v2-learning-robust-visual-features-without-supervision-model-explained/
5. https://blog.marvik.ai/2023/05/16/dinov2-exploring-self-supervised-vision-transformers/

Original papers:
1. https://arxiv.org/abs/2104.14294 Emerging Properties in Self-Supervised Vision Transformers (Dino)
2. https://arxiv.org/abs/2304.07193 DINOv2: Learning Robust Visual Features without Supervision
3. https://arxiv.org/abs/2309.16588 Vision Transformers Need Registers

https://arxiv.org/html/2405.18886v1 Compressing Large Language Models using Low Rank and Low Precision Decomposition #paper

https://github.com/staghado/vit.cpp Inference Vision Transformer (ViT) in plain C/C++ with ggml
https://github.com/ggerganov/ggml Tensor library for machine learning with Low-level cross-platform implementation
#Frameworks