⚡️ ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
▪page: https://res-adapter.github.io
▪paper: https://arxiv.org/abs/2403.02084
▪code: https://github.com/bytedance/res-adapter
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▪page: https://res-adapter.github.io
▪paper: https://arxiv.org/abs/2403.02084
▪code: https://github.com/bytedance/res-adapter
@Machine_learn
2206.13446.pdf
3 MB
Book: 📚Exercises in Machine Learning
Authors: Michael U. Gutmann
year: 2024
pages: 211
Tags: #ML
@Machine_learn
Authors: Michael U. Gutmann
year: 2024
pages: 211
Tags: #ML
@Machine_learn
Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning
🖥 Github: https://github.com/hikvision-research/3dvision
📕 Paper: https://arxiv.org/abs/2403.05117v1
🔥Dataset: https://paperswithcode.com/dataset/scanobjectnn
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🖥 Github: https://github.com/hikvision-research/3dvision
📕 Paper: https://arxiv.org/abs/2403.05117v1
🔥Dataset: https://paperswithcode.com/dataset/scanobjectnn
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Miguel_Morales_Grokking_Deep_Reinforcement_Learning_Manning_Publications.pdf
17.3 MB
Book: 📚grokking Deep Reinforcement Learning
Authors: Miguel Morales Foreword by Charles Isbell, Jr.
year: 2020
pages: 472
Tags: #RL #DRL
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Authors: Miguel Morales Foreword by Charles Isbell, Jr.
year: 2020
pages: 472
Tags: #RL #DRL
@Machine_learn
ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions
🖥 Github: https://github.com/Traffic-X/ViT-CoMer
📕 Paper: https://arxiv.org/pdf/2403.07392.pdf
✨ Tasks: https://paperswithcode.com/task/object-detection
🔥Dataset: https://paperswithcode.com/dataset/coco
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🖥 Github: https://github.com/Traffic-X/ViT-CoMer
📕 Paper: https://arxiv.org/pdf/2403.07392.pdf
✨ Tasks: https://paperswithcode.com/task/object-detection
🔥Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
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🏎 TripoSR: Fast 3D Object Reconstruction from a Single Image
▪page: https://tripo3d.ai
▪paper: https://drive.google.com/file/d/1LWlZPT2aASi9jHiGVhDSr4YCTANoFW5t/view
▪code: https://github.com/VAST-AI-Research/TripoSR
@Machine_learn
▪page: https://tripo3d.ai
▪paper: https://drive.google.com/file/d/1LWlZPT2aASi9jHiGVhDSr4YCTANoFW5t/view
▪code: https://github.com/VAST-AI-Research/TripoSR
@Machine_learn
با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
TSMixer: An All-MLP Architecture for Time Series Forecasting
Time-series datasets in real-world scenarios are inherently multivariate and riddled with intricate dynamics. While recurrent or attention-based deep learning models have been the go-to solution to address these complexities, recent discoveries have shown that even basic univariate linear models can surpass them in performance on standard academic benchmarks. As an extension of this revelation, the paper introduces the Time-Series Mixer TSMixer. This innovative design, crafted by layering multi-layer perceptrons, hinges on mixing operations across both time and feature axes, ensuring an efficient extraction of data nuances.
Upon application, TSMixer has shown promising results. Not only does it hold its ground against specialized state-of-the-art models on well-known benchmarks, but it also trumps leading alternatives in the challenging M5 benchmark, a dataset that mirrors the intricacies of retail realities. The paper's outcomes emphasize the pivotal role of cross-variate and auxiliary data in refining time series forecasting.
Paper link:
https://arxiv.org/abs/2303.06053
Code link: https://github.com/google-research/google-research/tree/master/tsmixer
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-tsmixer
@Machine_learn
Time-series datasets in real-world scenarios are inherently multivariate and riddled with intricate dynamics. While recurrent or attention-based deep learning models have been the go-to solution to address these complexities, recent discoveries have shown that even basic univariate linear models can surpass them in performance on standard academic benchmarks. As an extension of this revelation, the paper introduces the Time-Series Mixer TSMixer. This innovative design, crafted by layering multi-layer perceptrons, hinges on mixing operations across both time and feature axes, ensuring an efficient extraction of data nuances.
Upon application, TSMixer has shown promising results. Not only does it hold its ground against specialized state-of-the-art models on well-known benchmarks, but it also trumps leading alternatives in the challenging M5 benchmark, a dataset that mirrors the intricacies of retail realities. The paper's outcomes emphasize the pivotal role of cross-variate and auxiliary data in refining time series forecasting.
Paper link:
https://arxiv.org/abs/2303.06053
Code link: https://github.com/google-research/google-research/tree/master/tsmixer
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-tsmixer
@Machine_learn
arXiv.org
TSMixer: An All-MLP Architecture for Time Series Forecasting
Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning...
با عرض سلام دوستانی که مقاله برای Knowledge-based Systems می فرستن می تونن من رو به عنوان reviewer معرفی کنن تا مقالاتشون رو بررسی کنم.
https://www.sciencedirect.com/journal/knowledge-based-systems
@Machine_learn
https://www.sciencedirect.com/journal/knowledge-based-systems
@Machine_learn
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
🖥 Github: https://github.com/opengvlab/video-mamba-suite
📕 Paper: https://arxiv.org/abs/2403.09626v1
🔥Dataset: https://paperswithcode.com/dataset/egoschema
@Machine_learn
🖥 Github: https://github.com/opengvlab/video-mamba-suite
📕 Paper: https://arxiv.org/abs/2403.09626v1
🔥Dataset: https://paperswithcode.com/dataset/egoschema
@Machine_learn
با عرض سلام نياز به نفر دوم اين مقاله داريم.
ابتدا اركايو مقاله تا دو هفته ديگه فرستاده ميشه سپس براي Knowledge-based Systems فرستاده ميشه. كسايي كه نياز دارن به بنده مراجعه كنن
@Raminmousa
ابتدا اركايو مقاله تا دو هفته ديگه فرستاده ميشه سپس براي Knowledge-based Systems فرستاده ميشه. كسايي كه نياز دارن به بنده مراجعه كنن
@Raminmousa
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.
#interviews #datascience #python
https://www.tg-me.com/DataScienceQ
#interviews #datascience #python
https://www.tg-me.com/DataScienceQ
Telegram
Data Science Questions, Answers, Quizzes, Interviews
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.
Admin: @Hussein_Sheikho
Admin: @Hussein_Sheikho
[CVPR 2024] Diversity-aware Channel Pruning for StyleGAN Compression
🖥 Github: https://github.com/jiwoogit/dcp-gan
📕 Paper: https://arxiv.org/pdf/2403.13548v1.pdf
⭐️ Tasks: https://paperswithcode.com/task/image-generation
🔥Dataset: https://paperswithcode.com/dataset/ffhq
@Machine_learn
🖥 Github: https://github.com/jiwoogit/dcp-gan
📕 Paper: https://arxiv.org/pdf/2403.13548v1.pdf
⭐️ Tasks: https://paperswithcode.com/task/image-generation
🔥Dataset: https://paperswithcode.com/dataset/ffhq
@Machine_learn
🔥Grok-1 LLM .
Apache 2.0
▪ Model: https://dagshub.com/xai/grok-1
▪ Page: https://x.ai/blog/grok-os
▪ Code: https://github.com/xai-org/grok-1
▪ Hugging face:https://huggingface.co/xai-org/grok-1
@Machine_learn
Apache 2.0
▪ Model: https://dagshub.com/xai/grok-1
▪ Page: https://x.ai/blog/grok-os
▪ Code: https://github.com/xai-org/grok-1
▪ Hugging face:https://huggingface.co/xai-org/grok-1
@Machine_learn