How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification
https://machinelearningmastery.com/cost-sensitive-neural-network-for-imbalanced-classification/
https://machinelearningmastery.com/cost-sensitive-neural-network-for-imbalanced-classification/
MachineLearningMastery.com
How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification - MachineLearningMastery.com
Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems. Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model on…
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
Code: https://github.com/cambridgeltl/parameter-factorization
Paper: https://arxiv.org/pdf/2001.11453.pdf
Code: https://github.com/cambridgeltl/parameter-factorization
Paper: https://arxiv.org/pdf/2001.11453.pdf
GitHub
GitHub - cambridgeltl/parameter-factorization: Factorization of the neural parameter space for zero-shot multi-lingual and multi…
Factorization of the neural parameter space for zero-shot multi-lingual and multi-task transfer - GitHub - cambridgeltl/parameter-factorization: Factorization of the neural parameter space for zero...
End-to-end training of sparse deep neural networks with little-to-no performance loss.
Code: https://github.com/google-research/rigl
Paper: https://arxiv.org/abs/1911.11134v1
Code: https://github.com/google-research/rigl
Paper: https://arxiv.org/abs/1911.11134v1
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network
Paper: https://arxiv.org/abs/2002.00544v1
Code: https://github.com/uwjunqi/Tensor-Train-Neural-Network
Paper: https://arxiv.org/abs/2002.00544v1
Code: https://github.com/uwjunqi/Tensor-Train-Neural-Network
GitHub
GitHub - uwjunqi/Pytorch-Tensor-Train-Network: Jun and Huck's PyTorch-Tensor-Train Network Toolbox
Jun and Huck's PyTorch-Tensor-Train Network Toolbox - GitHub - uwjunqi/Pytorch-Tensor-Train-Network: Jun and Huck's PyTorch-Tensor-Train Network Toolbox
ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
https://ai.googleblog.com/2020/02/ml-fairness-gym-tool-for-exploring-long.html
Code: https://github.com/google/ml-fairness-gym/
Paper: https://github.com/google/ml-fairness-gym/blob/master/papers/acm_fat_2020_fairness_is_not_static.pdf
https://ai.googleblog.com/2020/02/ml-fairness-gym-tool-for-exploring-long.html
Code: https://github.com/google/ml-fairness-gym/
Paper: https://github.com/google/ml-fairness-gym/blob/master/papers/acm_fat_2020_fairness_is_not_static.pdf
Googleblog
ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
Introducing PyTorch3D: An open-source library for 3D deep learning
https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/
Code: https://github.com/facebookresearch/pytorch3d
https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/
Code: https://github.com/facebookresearch/pytorch3d
Facebook
Introducing PyTorch3D: An open-source library for 3D deep learning
We just released PyTorch3D, a new toolkit for researchers and engineers that’s fast and modular for 3D deep learning research.
PyRoboLearn: A Python Framework for Robot Learning Practitioners
Github: https://github.com/robotlearn/pyrobolearn
https://robotlearn.github.io/pyrobolearn/
Github: https://github.com/robotlearn/pyrobolearn
https://robotlearn.github.io/pyrobolearn/
GitHub
GitHub - robotlearn/pyrobolearn: PyRoboLearn: a Python framework for Robot Learning
PyRoboLearn: a Python framework for Robot Learning - robotlearn/pyrobolearn
TyDi QA: A Multilingual Question Answering Benchmark
https://ai.googleblog.com/2020/02/tydi-qa-multilingual-question-answering.html
https://ai.googleblog.com/2020/02/tydi-qa-multilingual-question-answering.html
blog.research.google
TyDi QA: A Multilingual Question Answering Benchmark
A Gentle Introduction to Threshold-Moving for Imbalanced Classification
https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/
https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/
TensorFlow Lattice: Flexible, controlled and interpretable ML
https://blog.tensorflow.org/2020/02/tensorflow-lattice-flexible-controlled-and-interpretable-ML.html
https://blog.tensorflow.org/2020/02/tensorflow-lattice-flexible-controlled-and-interpretable-ML.html
A new model and dataset for long-range memory
https://deepmind.com/blog/article/A_new_model_and_dataset_for_long-range_memory
https://deepmind.com/blog/article/A_new_model_and_dataset_for_long-range_memory
Deepmind
A new model and dataset for long-range memory
Throughout our lives, we build up memories that are retained over a diverse array of timescales, from minutes to months to years to decades. When reading a book, we can recall characters who were introduced many chapters ago, or in an earlier book in a series…
Bagging and Random Forest for Imbalanced Classification
https://machinelearningmastery.com/bagging-and-random-forest-for-imbalanced-classification/
https://machinelearningmastery.com/bagging-and-random-forest-for-imbalanced-classification/
Speeding up neural networks using TensorNetwork in Keras
https://blog.tensorflow.org/2020/02/speeding-up-neural-networks-using-tensornetwork-in-keras.html
https://blog.tensorflow.org/2020/02/speeding-up-neural-networks-using-tensornetwork-in-keras.html
AutoFlip: An Open Source Framework for Intelligent Video Reframing
https://ai.googleblog.com/2020/02/autoflip-open-source-framework-for.html
https://ai.googleblog.com/2020/02/autoflip-open-source-framework-for.html
research.google
AutoFlip: An Open Source Framework for Intelligent Video Reframing
Posted by Nathan Frey, Senior Software Engineer, Google Research, Los Angeles and Zheng Sun, Senior Software Engineer, Google Research, Mountain Vi...
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
https://senya-ashukha.github.io/pitfalls-uncertainty&ensembling
https://senya-ashukha.github.io/pitfalls-uncertainty&ensembling
Ashukha
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Optimizing infrastructure for neural recommendation at scale
https://ai.facebook.com/blog/-optimizing-infrastructure-for-neural-recommendation-at-scale/
https://ai.facebook.com/blog/-optimizing-infrastructure-for-neural-recommendation-at-scale/
Official Tensorflow implementation of the paper "Y-Autoencoders: disentangling latent representations via sequential-encoding»
Github: https://github.com/mpatacchiola/Y-AE
Paper: https://arxiv.org/abs/1907.10949
Github: https://github.com/mpatacchiola/Y-AE
Paper: https://arxiv.org/abs/1907.10949
ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters
https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/
https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/
Microsoft Research
ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters - Microsoft Research
The latest trend in AI is that larger natural language models provide better accuracy; however, larger models are difficult to train because of cost, time, and ease of code integration. Microsoft is releasing an open-source library called DeepSpeed, which…
How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1
https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/
https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/
Paperspace by DigitalOcean Blog
Tutorial on implementing YOLO v3 from scratch in PyTorch
Tutorial on building YOLO v3 detector from scratch detailing how to create the network architecture from a configuration file, load the weights and designing input/output pipelines.
How to Develop a Probabilistic Model of Breast Cancer Patient Survival
https://machinelearningmastery.com/how-to-develop-a-probabilistic-model-of-breast-cancer-patient-survival/
https://machinelearningmastery.com/how-to-develop-a-probabilistic-model-of-breast-cancer-patient-survival/
MachineLearningMastery.com
How to Develop a Probabilistic Model of Breast Cancer Patient Survival - MachineLearningMastery.com
Developing a probabilistic model is challenging in general, although it is made more so when there is skew in the distribution of cases, referred to as an imbalanced dataset.
The Haberman Dataset describes the five year or greater survival of breast cancer…
The Haberman Dataset describes the five year or greater survival of breast cancer…