InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
๐ฅ Github: https://github.com/jacobyhsi/InterpreTabNet
๐ Paper: https://arxiv.org/abs/2406.00426v1
@Machine_learn
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ุณูุงู
ุฏูุณุชุงู ุญุฏุงูู ู
ุงูู ู
ู ููููู NFT ู
ุงูู ูููู ูู ูู ฺูุฒู ฺฏูุฑุชูู ุจูุงุฏ. ุจู ูุธุฑู
ุงุณุงุณ ูููู ูุงุฑู ุจุฎูููู ุจุนุฏ ู
ุงูู ูููู. ูพุฑฺูู ูพุงููู ุงุฒ ุชู
ุงู
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ูุงุฑุฏู ูู ูุฑุณุชุงุฏูู ุจุฑุงู
ุจูุชุฑ ุจูุฏู.
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P2E game powered by TON and based on unique NFT
๐4
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๐ Real-time in-browser speech recognition
โชะกode: https://github.com/xenova/transformers.js/tree/v3/examples/webgpu-whisper
โชHf: https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu
@Machine_learn
โชะกode: https://github.com/xenova/transformers.js/tree/v3/examples/webgpu-whisper
โชHf: https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu
@Machine_learn
๐ฅ6๐2
๐ AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
๐ฅ Github: https://github.com/woooodyy/agentgym
๐ Paper: https://arxiv.org/abs/2406.04151v1
๐ฅProject: https://agentgym.github.io/
โก๏ธModel (AgentEvol-7B): https://huggingface.co/AgentGym/AgentEvol-7B
@Machine_learn
๐ฅProject: https://agentgym.github.io/
โก๏ธModel (AgentEvol-7B): https://huggingface.co/AgentGym/AgentEvol-7B
@Machine_learn
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๐3โค1
โชGithub: https://github.com/IntelLabs/MMPano
โชPaper: https://arxiv.org/abs/2406.01843
โชProject: https://zhipengcai.github.io/MMPano/
โชVideo: https://youtu.be/XDMNEzH4-Ec
@Machine_learn
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๐3
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๐ +750 $SPN as a first-time gift
Telegram
SpinnerCoin
P2E game powered by TON and based on unique NFT
๐ Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
๐ฅ Github: https://github.com/liamlian0727/usis10k
๐ Paper: https://arxiv.org/abs/2406.06039v1
@Machine_learn
@Machine_learn
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๐ Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
โชPaper: https://arxiv.org/abs/2406.05629
โชWebsite: https://mhamilton.net/denseav
โชCode: https://github.com/mhamilton723/DenseAV
โชVideo: https://youtu.be/wrsxsKG-4eE
@Machine_learn
โชPaper: https://arxiv.org/abs/2406.05629
โชWebsite: https://mhamilton.net/denseav
โชCode: https://github.com/mhamilton723/DenseAV
โชVideo: https://youtu.be/wrsxsKG-4eE
@Machine_learn
โ
pip install semantic-kernel
@Machine_learn
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๐6
๐ฅ Astrologers have announced a week of video generation models!
Following the hype around the Kling, Luma and Runway models, a new open source version of Open-Sora has been released.
Open-Sora 1.2 from Hpcoretech has been published on huggingface.
Basic moments:
The new 1.1B model is trained on 20M videos and generates videos up to 14 seconds long at 720p resolution.
โชDiffusion Model: https://huggingface.co/hpcai-tech/OpenSora-STDiT-v3
โชVAE model: https://huggingface.co/hpcai-tech/OpenSora-VAE-v1.2
โชTechnical report: https://github.com/hpcaitech/Open-Sora/blob/main/docs/report_03.md
โชDemo: https://huggingface.co/spaces/hpcai-tech/open-sora
@Machine_learn
Following the hype around the Kling, Luma and Runway models, a new open source version of Open-Sora has been released.
Open-Sora 1.2 from Hpcoretech has been published on huggingface.
Basic moments:
The new 1.1B model is trained on 20M videos and generates videos up to 14 seconds long at 720p resolution.
โชDiffusion Model: https://huggingface.co/hpcai-tech/OpenSora-STDiT-v3
โชVAE model: https://huggingface.co/hpcai-tech/OpenSora-VAE-v1.2
โชTechnical report: https://github.com/hpcaitech/Open-Sora/blob/main/docs/report_03.md
โชDemo: https://huggingface.co/spaces/hpcai-tech/open-sora
@Machine_learn
๐4
MajorTom-Core-S1RTC is a new satellite image standard and dataset that contains 1,469,955 images.
16 TB of radiometrically calibrated images.
โช HF: https://huggingface.co/Major-TOM
โช Github: https://github.com/ESA-PhiLab/Major-TOM/
โช Colab: https://colab.research.google.com/github/ESA-PhiLab/Major-TOM/blob/main/03-Filtering-in-Colab.ipynb
โช Paper: https://www.arxiv.org/abs/2402.12095
โช MajorTOM-Core-Viewer: https://huggingface.co/spaces/Major-TOM/MajorTOM-Core-Viewer
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๐5
TSI-Bench: Benchmarking Time Series Imputation
๐ฅ Github: https://github.com/WenjieDu/Awesome_Imputation
๐ Paper: https://arxiv.org/pdf/2406.12747v1.pdf
๐ฅDataset: https://github.com/WenjieDu/TSDB
@Machine_learn
๐ฅDataset: https://github.com/WenjieDu/TSDB
@Machine_learn
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๐1
ุจุง ุนุฑุถ ุณูุงู
ูพู ูุงุฏฺฏูุฑู ู
ุงุดูู ู ูุงุฏฺฏูุฑู ุนู
ูู ุจู ูู
ุฑุงู ูฃูฆ ูพุฑฺูู ุจุง ุฏุงูููู
ูุช ูุงุฑุณู ุฑู ุจุฑุงู ุฏูุณุชุงู ุชููู ูุฑุฏูู
ุงุฒ ุฏูุณุชุงู ูุณู ุฎูุงุณุช ู
ู ุชููู ุจู ุงูุฏู ุจูุฏู ูพูุงู
ุจุฏู.
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
@Raminmousa
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
@Raminmousa
๐11๐ฅ2โค1
Consistency Models Made Easy
๐ฅ Github: https://github.com/locuslab/ect
๐ Paper: https://arxiv.org/abs/2406.14548v1
๐ฅDataset: https://paperswithcode.com/dataset/cifar-10
@Machine_learn
๐ฅDataset: https://paperswithcode.com/dataset/cifar-10
@Machine_learn
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LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments
๐ฅ Github: https://github.com/bigai-nlco/langsuite
๐ Paper: https://arxiv.org/abs/2406.16294v1
๐ฅDataset: https://paperswithcode.com/dataset/ai2-thor
@Machine_learn
๐ฅDataset: https://paperswithcode.com/dataset/ai2-thor
@Machine_learn
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๐5โค1
Point-SAM: Promptable 3D Segmentation Model for Point Clouds
๐ฅ Github: https://github.com/zyc00/point-sam
๐ Paper: https://arxiv.org/abs/2406.17741v1
๐ฅDataset: https://paperswithcode.com/dataset/shapenet
@Machine_learn
๐ฅDataset: https://paperswithcode.com/dataset/shapenet
@Machine_learn
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โค2
ุจุง ุนุฑุถ ุณูุงู
ูพู ูุงุฏฺฏูุฑู ู
ุงุดูู ู ูุงุฏฺฏูุฑู ุนู
ูู ุจู ูู
ุฑุงู ูฃูฆ ูพุฑฺูู ุจุง ุฏุงูููู
ูุช ูุงุฑุณู ุฑู ุจุฑุงู ุฏูุณุชุงู ุชููู ูุฑุฏูู
ุงุฒ ุฏูุณุชุงู ูุณู ุฎูุงุณุช ู
ู ุชููู ุจู ุงูุฏู ุจูุฏู ูพูุงู
ุจุฏู.
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
@Raminmousa
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
@Raminmousa
โค2๐1