Interpretable Machine Learning
Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.
Read more »Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.
Read more »ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library. – GitHub – helblazer811/ManimML: ManimML is a project f…
Read more »Containers for machine learning. Contribute to replicate/cog development by creating an account on GitHub.
Read more »Due to the current horizontal business model that promotes increasing reliance on untrusted third-party Intellectual Properties (IPs), CAD tools, and design facilities, hardware Trojan attacks have become a serious threat to the semiconductor industry. De…
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Read more »Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the sur…
Read more »Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML ener…
Read more »We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer – a situation we refer to as mesa-optimization, a neologism we introduce in this paper. We believe that the possibility of mesa-…
Read more »Disclaimer: Feeling so-and-so about posting this on LW, but given how many people here work in ML or adjacent fields I might as well. …
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