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tiao.io | ||
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jxmo.io
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| | | | | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents. | |
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ssc.io
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| | | | | 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 computing Shapley-based data importance over ML pipelines. We introduce several approximations that lead to dramatic improvements in terms of computational speed. Finally, our experimental evaluation demonstrates that our methods are capable of data error discovery that is as effective as existing Monte Carlo baselines, and in some cases even outperform them. | |
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christopher-beckham.github.io
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| | | | | I wrote a self-contained implementation of NVIDIA's EDM diffusion model in a Jupyter notebook, as well as its associated sampling algorithms. I also discuss the rather confusing names used for real-world implementations of those algorithms. | |
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www.softdevtube.com
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| | | Software startups make global headlines every day. As technology companies succeed and grow, so do their software development departments. In your career, you might suddenly get the opportunity to lead teams: to become a software development manager. But this is often uncharted territory. How do you decide whether this career move towards management is right | ||