GANsformer: Generative Adversarial Transformers
Github: https://github.com/dorarad/gansformer
Paper: https://arxiv.org/abs/2103.01209v2
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Github: https://github.com/dorarad/gansformer
Paper: https://arxiv.org/abs/2103.01209v2
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PyTorch 1.8 Release, including Compiler and Distributed Training updates, and New Mobile Tutorials
https://pytorch.org/blog/pytorch-1.8-released/
@ArtificialIntelligencedl
https://pytorch.org/blog/pytorch-1.8-released/
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Speeding up Scikit-Learn Model Training
https://www.kdnuggets.com/2021/03/speed-up-scikit-learn-model-training.html
@ArtificialIntelligencedl
https://www.kdnuggets.com/2021/03/speed-up-scikit-learn-model-training.html
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KDnuggets
Speeding up Scikit-Learn Model Training - KDnuggets
If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization…
Attention Mechanism Exploits Temporal Contexts: Real-time 3D Human Pose Reconstruction (CVPR 2020 Oral)
https://sites.google.com/a/udayton.edu/jshen1/cvpr2020
Github: https://github.com/lrxjason/Attention3DHumanPose
Paper: https://arxiv.org/abs/2103.03170v1
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https://sites.google.com/a/udayton.edu/jshen1/cvpr2020
Github: https://github.com/lrxjason/Attention3DHumanPose
Paper: https://arxiv.org/abs/2103.03170v1
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TorchDrift: drift detection for PyTorch
https://torchdrift.org/
Github: https://github.com/torchdrift/torchdrift/
Example: https://torchdrift.org/notebooks/drift_detection_on_images.html
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https://torchdrift.org/
Github: https://github.com/torchdrift/torchdrift/
Example: https://torchdrift.org/notebooks/drift_detection_on_images.html
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Improving Graph Convolutional Networks with Lessons from Transformers
https://blog.einstein.ai/improving-graph-networks-with-transformers/
@ArtificialIntelligencedl
https://blog.einstein.ai/improving-graph-networks-with-transformers/
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Salesforce Research
Improving Graph Convolutional Networks with Lessons from Transformers
Transformer-inspired tips for enhancing the design of neural networks that process graph-structured data
Guide To GPyTorch: A Python Library For Gaussian Process Models
https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/
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https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/
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Analytics India Magazine
Guide To GPyTorch: A Python Library For Gaussian Process Models
GPyTorch is a PyTorch-based library for implementing Gaussian processes. It performs GP inference via Blackbox Matrix-Matrix multiplication.
XGBoost for Regression
https://machinelearningmastery.com/xgboost-for-regression/
@ArtificialIntelligencedl
https://machinelearningmastery.com/xgboost-for-regression/
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Know your data much faster with the new Sweetviz Python library
https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html
@ArtificialIntelligencedl
https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html
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KDnuggets
Know your data much faster with the new Sweetviz Python library
One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and…
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Github: https://github.com/PaddlePaddle/PaddleClas
Paper: https://arxiv.org/abs/2103.05959v1
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Github: https://github.com/PaddlePaddle/PaddleClas
Paper: https://arxiv.org/abs/2103.05959v1
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GitHub
GitHub - PaddlePaddle/PaddleClas: A treasure chest for visual classification and recognition powered by PaddlePaddle
A treasure chest for visual classification and recognition powered by PaddlePaddle - PaddlePaddle/PaddleClas
TimeSformer: A new architecture for video understanding
https://ai.facebook.com/blog/timesformer-a-new-architecture-for-video-understanding/
Paper: https://arxiv.org/abs/2102.05095
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https://ai.facebook.com/blog/timesformer-a-new-architecture-for-video-understanding/
Paper: https://arxiv.org/abs/2102.05095
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ReDet: A Rotation-equivariant Detector for Aerial Object Detection
Github: https://github.com/csuhan/ReDet
Paper: https://arxiv.org/abs/2103.07733v1
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Github: https://github.com/csuhan/ReDet
Paper: https://arxiv.org/abs/2103.07733v1
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Forwarded from TensorFlow
Contactless Sleep Sensing in Nest Hub
https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
@tensorflowblog
https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
@tensorflowblog
Googleblog
Contactless Sleep Sensing in Nest Hub
Natural Language Processing Pipelines, Explained
https://www.kdnuggets.com/2021/03/natural-language-processing-pipelines-explained.html
@ArtificialIntelligencedl
https://www.kdnuggets.com/2021/03/natural-language-processing-pipelines-explained.html
@ArtificialIntelligencedl
UPANets: Learning from the Universal Pixel Attention Networks Edit social preview
Github: https://github.com/hanktseng131415go/UPANets
Paper: https://arxiv.org/pdf/2103.08640.pdf
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Github: https://github.com/hanktseng131415go/UPANets
Paper: https://arxiv.org/pdf/2103.08640.pdf
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👍1
Training GANs with Stronger Augmentations via Contrastive Discriminator Edit social preview
Github: https://github.com/jh-jeong/ContraD
Paper: https://arxiv.org/abs/2103.09742
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Github: https://github.com/jh-jeong/ContraD
Paper: https://arxiv.org/abs/2103.09742
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You Only Look One-level Feature
Github: https://github.com/megvii-model/YOLOF
Object detector without FPN: https://github.com/chensnathan/YOLOF
Paper: https://arxiv.org/abs/2103.09460v1
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Github: https://github.com/megvii-model/YOLOF
Object detector without FPN: https://github.com/chensnathan/YOLOF
Paper: https://arxiv.org/abs/2103.09460v1
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A Gentle Introduction to XGBoost Loss Functions
https://machinelearningmastery.com/xgboost-loss-functions/
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https://machinelearningmastery.com/xgboost-loss-functions/
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MachineLearningMastery.com
A Gentle Introduction to XGBoost Loss Functions - MachineLearningMastery.com
XGBoost is a powerful and popular implementation of the gradient boosting ensemble algorithm. An important aspect in configuring XGBoost models is the choice of loss function that is minimized during the training of the model. The loss function must be matched…
👍1
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
Github: https://github.com/594422814/TransformerTrack
Paper: https://arxiv.org/abs/2103.11681v1
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Github: https://github.com/594422814/TransformerTrack
Paper: https://arxiv.org/abs/2103.11681v1
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Pose-Transfer
Github: https://github.com/tengteng95/Pose-Transfer
Paper: https://arxiv.org/abs/1904.03349
@ArtificialIntelligencedl
Github: https://github.com/tengteng95/Pose-Transfer
Paper: https://arxiv.org/abs/1904.03349
@ArtificialIntelligencedl