https://web.stanford.edu/~jurafsky/slp3/
#books Speech and Language Processing (3rd ed. draft)
Dan Jurafsky and James H. Martin
#books Speech and Language Processing (3rd ed. draft)
Dan Jurafsky and James H. Martin
NVidia monitoring #Tools :
1. GPUStat https://github.com/wookayin/gpustat
2. Nvtop https://github.com/Syllo/nvtop
3. NVITOP https://github.com/XuehaiPan/nvitop
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
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
arXiv.org
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce...
🔥1
https://arxiv.org/pdf/2408.04840v1
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
#Paper
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
Dimensionality reduction techniques in one place #FYI #Tips
Encord
Top 12 Dimensionality Reduction Techniques for Machine Learning
Dimensionality reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables
🔥1
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://github.com/rapidsai/cuml
https://docs.rapids.ai/api/cuml/stable/
GitHub
GitHub - rapidsai/cuml: cuML - RAPIDS Machine Learning Library
cuML - RAPIDS Machine Learning Library. Contribute to rapidsai/cuml development by creating an account on GitHub.
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
https://numba.pydata.org/ #Frameworks #library
❤1👏1
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
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
Medium
Emerging Properties in Self-Supervised Vision Transformers (DINO) — Paper Summary
Hi Everyone! Today, we’ll unravel the complexities of an intriguing approach in the realm of self-supervised learning, delving into a groundbreaking paper titled “Emerging Properties in…
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
https://github.com/ggerganov/ggml Tensor library for machine learning with Low-level cross-platform implementation
#Frameworks
GitHub
GitHub - staghado/vit.cpp: Inference Vision Transformer (ViT) in plain C/C++ with ggml
Inference Vision Transformer (ViT) in plain C/C++ with ggml - staghado/vit.cpp
https://arxiv.org/abs/2412.11768
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
arXiv.org
No More Adam: Learning Rate Scaling at Initialization is All You Need
In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum...
Deep Learning
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…
https://www.samarkhanna.com/ExPLoRA/ Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
#Paper #Framework
#Paper #Framework
Samarkhanna
ExPLoRA
ExPLoRA: Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
https://medium.com/version-1/the-rise-of-large-action-models-lams-how-ai-can-understand-and-execute-human-intentions-f59c8e78bc09
Large Action Models
Large Action Models
Medium
The Rise of Large Action Models, LAMs: How AI Can Understand and Execute Human Intentions?
A hot topic and development in the realm artificial intelligence (AI) is Large Action Models, also referred as Large Agentic Models or LAMs…
https://arxiv.org/pdf/2411.07975
JanusFlow: Harmonizing Autoregression and Rectified Flow
for Unified Multimodal Understanding and Generation
#Paper
Finally multimodality on input and output!
JanusFlow: Harmonizing Autoregression and Rectified Flow
for Unified Multimodal Understanding and Generation
#Paper
Finally multimodality on input and output!