Revisiting the Minimalist Approach to Offline Reinforcement Learning
🖥 Github: https://github.com/tinkoff-ai/rebrac
📕 Paper: https://arxiv.org/pdf/2305.09836v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/d4rl
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/tinkoff-ai/rebrac
📕 Paper: https://arxiv.org/pdf/2305.09836v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/d4rl
https://t.iss.one/DataScienceT
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🔥Platypus: Quick, Cheap, and Powerful Refinement of LLMs
Family of fine-tuned and merged LLMs that achieves the strongest performance and currently stands at first place in HuggingFace's
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
🖥 Github: https://github.com/arielnlee/Platypus
💻 Project: https://platypus-llm.github.io/
📕 Paper: https://arxiv.org/abs/2308.07317v1
⭐️ Dataset: https://huggingface.co/datasets/garage-bAInd/Open-Platypus
https://t.iss.one/DataScienceT
Family of fine-tuned and merged LLMs that achieves the strongest performance and currently stands at first place in HuggingFace's
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
🖥 Github: https://github.com/arielnlee/Platypus
💻 Project: https://platypus-llm.github.io/
📕 Paper: https://arxiv.org/abs/2308.07317v1
⭐️ Dataset: https://huggingface.co/datasets/garage-bAInd/Open-Platypus
https://t.iss.one/DataScienceT
👍3
Forwarded from Data Science Machine Learning Data Analysis
Encyclopedia of Data Science and Machine Learning (2023)
This book was released two days ago and this book is more than 3400 pages.
With this book, you can become a first-class professional data scientist
The price of the book is $3,400
To get a discount of up to 95%, contact me immediately
Contact @hussein_sheikho
This book was released two days ago and this book is more than 3400 pages.
With this book, you can become a first-class professional data scientist
The price of the book is $3,400
To get a discount of up to 95%, contact me immediately
Contact @hussein_sheikho
👍8👎4🏆1
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✍ EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization.
🖥 Github: https://github.com/zjunlp/easyedit
📕 Paper: https://arxiv.org/abs/2308.07269v1
⭐️ Demo: https://knowlm.zjukg.cn/demo_edit
🎓Online Tutorial: https://colab.research.google.com/drive/1zcj8YgeqttwkpfoHXz9O9_rWxFFufXSO?usp=sharing
☑️ Docs: https://zjunlp.gitbook.io/easyedit
🤓 Dataset: https://drive.google.com/file/d/1IVcf5ikpfKuuuYeedUGomH01i1zaWuI6/view?usp=sharing
https://t.iss.one/DataScienceT
EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization.
🖥 Github: https://github.com/zjunlp/easyedit
📕 Paper: https://arxiv.org/abs/2308.07269v1
⭐️ Demo: https://knowlm.zjukg.cn/demo_edit
🎓Online Tutorial: https://colab.research.google.com/drive/1zcj8YgeqttwkpfoHXz9O9_rWxFFufXSO?usp=sharing
☑️ Docs: https://zjunlp.gitbook.io/easyedit
🤓 Dataset: https://drive.google.com/file/d/1IVcf5ikpfKuuuYeedUGomH01i1zaWuI6/view?usp=sharing
https://t.iss.one/DataScienceT
👍4❤1
🧑💻DeciCoder: A new open-source LLM, specialized for generating code in Python, Java, and Javascript..
- parameters: 1 B
- dataset: 'The Stack' dataset
- supports: Python, Javascript, Java
- context: 2048 tokens
▪Model
▪Colab
▪Dataset
https://t.iss.one/DataScienceT
- parameters: 1 B
- dataset: 'The Stack' dataset
- supports: Python, Javascript, Java
- context: 2048 tokens
▪Model
▪Colab
▪Dataset
https://t.iss.one/DataScienceT
👍5❤2
✔️ DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
🖥 Github: https://github.com/parskatt/dedode
☑️ TensorRT: https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT
📕 Paper: https://arxiv.org/abs/2308.08479
⭐️ Demos: https://github.com/Parskatt/DeDoDe/blob/main/demo
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/parskatt/dedode
☑️ TensorRT: https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT
📕 Paper: https://arxiv.org/abs/2308.08479
⭐️ Demos: https://github.com/Parskatt/DeDoDe/blob/main/demo
https://t.iss.one/DataScienceT
👍2❤1
Forwarded from Python | Machine Learning | Coding | R
Our page on Reddit App
https://www.reddit.com/r/DataSciencePy
https://www.reddit.com/r/DataSciencePy
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💨CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
🖥 Github: https://github.com/qiuyu96/codef
☑️ Project: https://qiuyu96.github.io/CoDeF/
📕 Paper: https://arxiv.org/abs/2308.07926
⭐️ Demo: https://ezioby.github.io/CoDeF_Demo/
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/qiuyu96/codef
☑️ Project: https://qiuyu96.github.io/CoDeF/
📕 Paper: https://arxiv.org/abs/2308.07926
⭐️ Demo: https://ezioby.github.io/CoDeF_Demo/
https://t.iss.one/DataScienceT
👍3
EQ-Net: Elastic Quantization Neural Networks
🖥 Github: https://github.com/xuke225/eq-net
📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/xuke225/eq-net
📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
❤🔥1👍1
☄️Dataset Quantization
DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.
🖥 Github: https://github.com/magic-research/dataset_quantization
📕 Paper: https://arxiv.org/abs/2308.10524v1
☑️ Dataset: https://paperswithcode.com/dataset/gsm8k
https://t.iss.one/DataScienceT
DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.
git clone https://github.com/vimar-gu/DQ.git
cd DQ🖥 Github: https://github.com/magic-research/dataset_quantization
📕 Paper: https://arxiv.org/abs/2308.10524v1
☑️ Dataset: https://paperswithcode.com/dataset/gsm8k
https://t.iss.one/DataScienceT
👍5❤2
Forwarded from Data Science Books
Machine Learning for Data Science Handbook (2023)
This book is available now only in paid channel
Pages: 975 pages
Rate: ⭐️⭐️⭐️⭐️⭐️
Cost of subscription in Paid channel is 5$ for one time and forever
Channel link: https://t.iss.one/+LnCmAFJO3tNmYjUy
Paid channel contain important book and udemy and other courses as zip files
Welcome all
Contact @Hussein_sheikho
This book is available now only in paid channel
Pages: 975 pages
Rate: ⭐️⭐️⭐️⭐️⭐️
Cost of subscription in Paid channel is 5$ for one time and forever
Channel link: https://t.iss.one/+LnCmAFJO3tNmYjUy
Paid channel contain important book and udemy and other courses as zip files
Welcome all
Contact @Hussein_sheikho
👍4
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
🖥 Github: https://github.com/osvai/ske2grid
📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/ucf101
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/osvai/ske2grid
📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/ucf101
https://t.iss.one/DataScienceT
👍2
EQ-Net: Elastic Quantization Neural Networks
🖥 Github: https://github.com/xuke225/eq-net
📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/xuke225/eq-net
📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
👍3❤1
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🏅MixSort
MixSort is the proposed baseline tracker in SportMOT.
🖥 Github: https://github.com/MCG-NJU/MixSort
📕 Paper: https://arxiv.org/pdf/2304.05170.pdf
⭐️ SportsMOT: https://github.com/MCG-NJU/SportsMOT
https://t.iss.one/DataScienceT
MixSort is the proposed baseline tracker in SportMOT.
🖥 Github: https://github.com/MCG-NJU/MixSort
📕 Paper: https://arxiv.org/pdf/2304.05170.pdf
⭐️ SportsMOT: https://github.com/MCG-NJU/SportsMOT
https://t.iss.one/DataScienceT
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⚡prompt2model - Generate Deployable Models from Instructions
prompt2model - Generate Deployable Models from Natural Language Instructions
🖥 Github: https://github.com/neulab/prompt2model
📕 Paper: https://arxiv.org/abs/2308.12261v1
⭐️ Demo: https://github.com/facebookresearch/sonar#usage
☑️ Dataset: https://paperswithcode.com/dataset/mconala
https://t.iss.one/DataScienceT
prompt2model - Generate Deployable Models from Natural Language Instructions
pip install prompt2model🖥 Github: https://github.com/neulab/prompt2model
📕 Paper: https://arxiv.org/abs/2308.12261v1
⭐️ Demo: https://github.com/facebookresearch/sonar#usage
☑️ Dataset: https://paperswithcode.com/dataset/mconala
https://t.iss.one/DataScienceT
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🔥Dense Text-to-Image Generation with Attention Modulation
DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle dense captions while offering control over the scene layout.
🖥 Github: https://github.com/naver-ai/densediffusion
📕 Paper: https://arxiv.org/abs/2308.12964v1
⭐️ Dataset: https://paperswithcode.com/dataset/coco
https://t.iss.one/DataScienceT
DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle dense captions while offering control over the scene layout.
🖥 Github: https://github.com/naver-ai/densediffusion
📕 Paper: https://arxiv.org/abs/2308.12964v1
⭐️ Dataset: https://paperswithcode.com/dataset/coco
https://t.iss.one/DataScienceT
👍3
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
🖥 Github: https://github.com/llvy21/duic
📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/pixel-art
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/llvy21/duic
📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/pixel-art
https://t.iss.one/DataScienceT
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