Benchmarking

Algorithms

1. scikit bench - "scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms."

Numpy Blas:

1. How do i know which version of blas is installed
2. Benchmark OpenBLAS, Intel MKL vs ATLAS

1. Another comparison
2. 

GLUE:

1. Glue / super glue

State of the art in AI:

1. In terms of domain X datasets

Cloud providers:

• Part 1, part 2 y gensim

Datasets:

• EFF FF Benchmarks in AI

Hardware:

• Nvidia 1070 vs 1080 vs 2080
• Cpu vs GPU benchmarking for CNN\Test\LTSM\BDLTSM - google and amazon vs gpu
• Nvidia GPUs - titax Xp\1080TI\1070 on googlenet
• March\17 - Nvidia GPUs for desktop, in terms of price and cuda units, the bottom line is 1060-1080.
• Another bench up to 2013 - regarding many GPUS vs CPUs in terms of BW

Platforms

• Cntk vs tensorflow
• CNTK, TEnsor, torch, etc on cpu and gpu

Algorithms:

• Comparing accuracy, speed, memory and 2D visualization of classifiers:

SVM, k-nearest neighbors, Random Forest, AdaBoost Classifier, Gradient Boosting, Naive, Bayes, LDA, QDA, RBMs, Logistic Regression, RBM + Logistic Regression Classifier

• LSTM vs cuDNN LS1TM - batch size of power 2 matters, the latter is faster.

Scaling networks and predicting performance of NN:

• A great overview of NN types, but the idea behind the video is to create a system that can predict train time and possibly accuracy when scaling networks using multiple GPUs, there is also a nice slide about general hardware recommendations.

NLP

• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

Multi-Task Learning

1. Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (Yarin Gal) GitHub - "In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task’s loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. "
2. Ruder on Multi Task Learning - "By sharing representations between related tasks, we can enable our model to generalize better on our original task. This approach is called Multi-Task Learning (MTL) and will be the topic of this blog post."