Benchmarks

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• Evaluation
  • Evaluation - Measures
    • Accuracy
    • Precision & Recall (Sensitivity)
    • Specificity
  • Bias and Variances
  • Explainable Artificial Intelligence (XAI)
  • Train, Validate, and Test
  • Model Monitoring
• Datasets
• Case Studies
  • Gaming
• Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends
• Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science
• DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
• Benchmarking 20 Machine Learning Models Accuracy and Speed | Marc Borowczak - Data Science Central
• Benchmarking deep learning models on large healthcare datasets | S. Purushotham, C. Meng, Z. Chea, and Y. Liu

Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019 Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information Consulting Assistant Professor Bill MacCartney Senior Engineering Manager, Apple

Kaggle Reading Group : An Open Source AutoML Benchmark | Kaggle This week we're starting a new paper: An Open Source AutoML Benchmark by Gijsbers et al from the 2019 ICML Workshop on Automated Machine Learning.

Machine Learning Model Evaluation Metrics MARIA KHALUSOVA | DEVELOPER ADVOCATE AT JETBRAINS Choosing the right evaluation metric for your machine learning project is crucial, as it decides which model you’ll ultimately use. Those coming to ML from software development are often self-taught, but practice exercises and competitions generally dictate the evaluation metric. In a real-world scenario, how do you choose an appropriate metric? This talk will explore the important evaluation metrics used in regression and classification tasks, their pros and cons, and how to make a smart decision.

Characterization and Benchmarking of Deep Learning In this video from the HPC User Forum in Milwaukee, Natalia Vassilieva from HP Labs presents: Characterization and Benchmarking of Deep Learning.

General Language Understanding Evaluation (GLUE)

• General Language Understanding Evaluation (GLUE)
• Natural Language Processing (NLP)

The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems... like picking out the names of people and organizations in a sentence and figuring out what a pronoun like “it” refers to when there are multiple potential antecedents. GLUE consists of: A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty, A diagnostic dataset designed to evaluate and analyze model performance with respect to a wide range of linguistic phenomena found in natural language, and A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.

The Stanford Question Answering Dataset (SQuAD)

• The Stanford Question Answering Dataset (SQuAD)

ReAding Comprehension (RACE)

• ReAding Comprehension {RACE)

MLPerf

• MLPerf benchmarks for measuring training and inference performance of ML hardware, software, and services.

Procgen

• OpenAI
• OpenAI’s Procgen Benchmark prevents AI model overfitting | Kyle Wiggers - VentureBeat a set of 16 procedurally generated environments that measure how quickly a model learns generalizable skills. It builds atop the startup’s CoinRun toolset, which used procedural generation to construct sets of training and test levels.

OpenAI previously released Neural MMO, a “massively multiagent” virtual training ground that plops agents in the middle of an RPG-like world, and Gym, a proving ground for algorithms for reinforcement learning (which involves training machines to do things based on trial and error). More recently, it made available SafetyGym, a suite of tools for developing AI that respects safety constraints while training, and for comparing the “safety” of algorithms and the extent to which those algorithms avoid mistakes while learning.