Explore >> Select a destination
|
You are here 
|
analytixon.com | ||
| | | | |
ssc.io
|
|
| | | | | When a machine learning (ML) model exhibits poor quality (e.g., poor accuracy or fairness), the problem can often be traced back to errors in the training data. Being able to discover the data examples that are the most likely culprits is a fundamental concern that has received a lot of attention recently. One prominent way to measure 'data importance' with respect to model quality is the Shapley value. Unfortunately, existing methods only focus on the ML model in isolation, without considering the broader ML pipeline for data preparation and feature extraction, which appears in the majority of real-world ML code. This presents a major limitation to applying existing methods in practical settings. In this paper, we propose Canonpipe, a method for efficiently... | |
| | | | |
www.layer9.org
|
|
| | | | | Group blog for the computer networking and systems research community. | |
| | | | |
teddykoker.com
|
|
| | | | | Gradient-descent-based optimizers have long been used as the optimization algorithm of choice for deep learning models. Over the years, various modifications to the basic mini-batch gradient descent have been proposed, such as adding momentum or Nesterovs Accelerated Gradient (Sutskever et al., 2013), as well as the popular Adam optimizer (Kingma & Ba, 2014). The paper Learning to Learn by Gradient Descent by Gradient Descent (Andrychowicz et al., 2016) demonstrates how the optimizer itself can be replac... | |
| | | | |
www.jeremymorgan.com
|
|
| | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | ||
