A Quick Review of Deep Learning Algorithms and Architectures

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Artificial Intelligence with Python
#book #2022
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🛠 ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Github: https://github.com/jiacheng-ye/zerogen

Paper: https://arxiv.org/abs/2202.07922v1

Dataset: https://paperswithcode.com/dataset/sst

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🐭 pymdp: A Python library for active inference in discrete state spaces

Github: https://github.com/infer-actively/pymdp

Paper: https://arxiv.org/abs/2201.03904v1

Docs: https://pymdp-rtd.readthedocs.io/

Tasks: https://paperswithcode.com/task/bayesian-inference

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Crazy family 🤣
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✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning

Github: https://github.com/sense-x/uniformer

Paper: https://arxiv.org/abs/2201.04676v1

Tasks: https://paperswithcode.com/dataset/kinetics-600

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Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation

Github: https://github.com/tzer-anonbot/tzer

Docs: https://tzer.readthedocs.io/en/latest/markdown/artifact.html

Paper: https://arxiv.org/abs/2202.09947v1


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Python Tricks
#book #python
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#Deep_learning #book
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EEG microstate and functional brain network features for
classification of epilepsy and PNES #EEG #Thesis @Machine_learn

Deep Learning for EEG Data Analytics: A Survey #EEG #Deeplearing #Paper @Machine_learn

A Survey on EEG Signal Processing Techniques and Machine
Learning: Applications to the Neurofeedback of
Autobiographical Memory Deficits in Schizophrenia #EEG #ML #DL @Machine_learn

DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association

Github: https://github.com/wangxiyang2022/DeepFusionMOT

Paper: https://arxiv.org/abs/2202.12100v1

Dataset: https://paperswithcode.com/dataset/kitti

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Overcoming catastrophic forgetting in neural networks

Github: https://github.com/ContinualAI/avalanche

Paper: https://arxiv.org/abs/1612.00796v2

Dataset: https://paperswithcode.com/dataset/asc-til-19-tasks

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When the code works anyway



♥️😂 @Machine_learn

Machine Learning for Mechanical Ventilation Control


http://ai.googleblog.com/2022/02/machine-learning-for-mechanical.html

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Abstract: SuperpixelGridMasks. It is a data augmentation approach which permits to generate various complementary images from original sensor-based data of varied natures e.g. X-Ray scans, vehicular scenes, people images (see data samples). This approach allows to increase the size of your image-based training datasets towards expecting better performances in your analysis tasks. Experiments have shown that the approach can be efficient for image classification tasks.

Link: https://www.researchgate.net/publication/360062941_SuperpixelGridCut_SuperpixelGridMean_and_SuperpixelGridMix_Data_Augmentation
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