Symbolic Music Generation with Diffusion Models
Github: https://github.com/magenta/symbolic-music-diffusion
Paper: https://arxiv.org/abs/2103.16091v1
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Github: https://github.com/magenta/symbolic-music-diffusion
Paper: https://arxiv.org/abs/2103.16091v1
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How to Perform Malaria Classification using TensorFlow 2 and Keras in Python
https://www.thepythoncode.com/article/malaria-cells-classification
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https://www.thepythoncode.com/article/malaria-cells-classification
@ArtificialIntelligencedl
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Easy, Open-Source AutoML in Python with EvalML
https://innovation.alteryx.com/introducing-evalml/
@ArtificialIntelligencedl
https://innovation.alteryx.com/introducing-evalml/
@ArtificialIntelligencedl
The surprising impact of mask-head architecture on novel class segmentation
Github: https://github.com/zeliu98/Group-Free-3D
Paper: https://arxiv.org/abs/2104.00678v1
@ArtificialIntelligencedl
Github: https://github.com/zeliu98/Group-Free-3D
Paper: https://arxiv.org/abs/2104.00678v1
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👍1
Guide To PyOD: A Python Toolkit For Outlier Detection
https://analyticsindiamag.com/guide-to-pyod-a-python-toolkit-for-outlier-detection/
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https://analyticsindiamag.com/guide-to-pyod-a-python-toolkit-for-outlier-detection/
@ArtificialIntelligencedl
Hypercorrelation Squeeze for Few-Shot Segmentation
Github: https://github.com/juhongm999/hsnet
Paper: https://arxiv.org/abs/2104.01538v1
Project: https://cvlab.postech.ac.kr/research/HSNet/
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Github: https://github.com/juhongm999/hsnet
Paper: https://arxiv.org/abs/2104.01538v1
Project: https://cvlab.postech.ac.kr/research/HSNet/
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👍1
Fourier Image Transformer
Github: https://github.com/juglab/FourierImageTransformer
Paper: https://arxiv.org/abs/2104.02555v1
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Github: https://github.com/juglab/FourierImageTransformer
Paper: https://arxiv.org/abs/2104.02555v1
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SOLD² - Self-supervised Occlusion-aware Line Description and Detection
Github: https://github.com/cvg/SOLD2
Paper: https://arxiv.org/abs/2104.03362
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Github: https://github.com/cvg/SOLD2
Paper: https://arxiv.org/abs/2104.03362
@ArtificialIntelligencedl
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Github: https://github.com/speechbrain/speechbrain/tree/develop/recipes/Voicebank/enhance/MetricGAN
Paper: https://arxiv.org/abs/1905.04874
@ArtificialIntelligencedl
Github: https://github.com/speechbrain/speechbrain/tree/develop/recipes/Voicebank/enhance/MetricGAN
Paper: https://arxiv.org/abs/1905.04874
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🔥1
AutoGL: A Library for Automated Graph Learning
Github: https://github.com/THUMNLab/AutoGL
Paper: https://arxiv.org/abs/2104.04987v1
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Github: https://github.com/THUMNLab/AutoGL
Paper: https://arxiv.org/abs/2104.04987v1
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Compact Transformers
Github: https://github.com/SHI-Labs/Compact-Transformers
Paper: https://arxiv.org/abs/2104.05704
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Github: https://github.com/SHI-Labs/Compact-Transformers
Paper: https://arxiv.org/abs/2104.05704
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Shape and Material Capture at Home, CVPR 2021
Github: https://github.com/dlichy/ShapeAndMaterial
Paper: https://arxiv.org/abs/2104.06397v1
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Github: https://github.com/dlichy/ShapeAndMaterial
Paper: https://arxiv.org/abs/2104.06397v1
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How To Code RNN And LSTM Neural Networks In Python
https://www.nbshare.io/notebook/249468051/How-To-Code-RNN-and-LSTM-Neural-Networks-in-Python/
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https://www.nbshare.io/notebook/249468051/How-To-Code-RNN-and-LSTM-Neural-Networks-in-Python/
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Generating Cats with StyleGAN on AWS SageMaker
https://devopstar.com/2019/02/25/generating-cats-with-stylegan-on-aws-sagemaker
Code: https://github.com/t04glovern/stylegan
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https://devopstar.com/2019/02/25/generating-cats-with-stylegan-on-aws-sagemaker
Code: https://github.com/t04glovern/stylegan
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AI predicts effective drug combinations to fight complex diseases faster
https://ai.facebook.com/blog/ai-predicts-effective-drug-combinations-to-fight-complex-diseases-faster/
Github: https://github.com/facebookresearch/CPA
Paper: https://www.biorxiv.org/content/10.1101/2021.04.14.439903v1
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https://ai.facebook.com/blog/ai-predicts-effective-drug-combinations-to-fight-complex-diseases-faster/
Github: https://github.com/facebookresearch/CPA
Paper: https://www.biorxiv.org/content/10.1101/2021.04.14.439903v1
@ArtificialIntelligencedl
Essential Math for Data Science: Linear Transformation with Matrices
https://hadrienj.github.io/posts/Essential-Math-for-Data-Science-linear_transformations/
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https://hadrienj.github.io/posts/Essential-Math-for-Data-Science-linear_transformations/
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🐙 OCTIS : Optimizing and Comparing Topic Models is Simple!
Github: https://github.com/mind-Lab/octis
Paper: https://www.aclweb.org/anthology/2021.eacl-demos.31/
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Github: https://github.com/mind-Lab/octis
Paper: https://www.aclweb.org/anthology/2021.eacl-demos.31/
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GitHub
GitHub - MIND-Lab/OCTIS: OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted…
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track) - MIND-Lab/OCTIS
DomainBed is a PyTorch suite containing benchmark datasets and algorithms for domain generalization
Github: https://github.com/facebookresearch/DomainBed
Paper: https://arxiv.org/abs/2104.09937v1
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Github: https://github.com/facebookresearch/DomainBed
Paper: https://arxiv.org/abs/2104.09937v1
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GitHub
GitHub - facebookresearch/DomainBed: DomainBed is a suite to test domain generalization algorithms
DomainBed is a suite to test domain generalization algorithms - facebookresearch/DomainBed
Forwarded from TensorFlow
Evolving Reinforcement Learning Algorithms
https://ai.googleblog.com/2021/04/evolving-reinforcement-learning.html
@tensorflowblog
https://ai.googleblog.com/2021/04/evolving-reinforcement-learning.html
@tensorflowblog
research.google
Evolving Reinforcement Learning Algorithms
Posted by John D. Co-Reyes, Research Intern and Yingjie Miao, Senior Software Engineer, Google Research A long-term, overarching goal of research i...
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
Github: https://github.com/W-zx-Y/DANNet
Paper: https://arxiv.org/abs/2104.10834
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Github: https://github.com/W-zx-Y/DANNet
Paper: https://arxiv.org/abs/2104.10834
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