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


@Machine_learn

Python Tricks
#book #python
@Machine_learn

#Deep_learning #book
@Machine_learn

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

@Machine_learn

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

@Machine_learn

When the code works anyway



♥️😂 @Machine_learn

Machine Learning for Mechanical Ventilation Control


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

@Machine_learn

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
@Machine_learn

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Github: https://github.com/hpcaitech/fastfold

Paper: https://arxiv.org/abs/2203.00854v1

@Machine_learn

Uncertainty Estimation for Heatmap-based Landmark Localization

Github: https://github.com/pykale/pykale

Documentatuin: https://github.com/pykale/pykale

Paper: https://arxiv.org/abs/2203.02351v1

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

@Machine_learn

https://web.njit.edu/~ym329/dlg_book/dlg_book.pdf

Deep Learning on Graphs

📖 Book

#deeplearning #DL

@Machine_learn

Cryptocurrency liquidity and volatility interrelationships during
the COVID-19 pandemic #Paper @Machine_learn

Periodicity in Cryptocurrency Volatility and Liquidity #Paper @Machine_learn