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Last Updated on January 9, 2023 Transformer is a recent breakthrough in neural machine translation. Natural languages are complicated. A word in one language can be translated into multiple words in another, depending on the context. But what exactly a context is, and how you can teach the computer to understand the context was a …
To regulate the distribution shift experience by learning-based controllers, we seek a mechanism for constraining the agent to regions of high data density throughout its trajectory (left). Here, we present an approach which achieves this goal by combining features of density models (middle) and Lyapunov functions (right). In order to make use of machine learning …
Read more “Keeping Learning-Based Control Safe by Regulating Distributional Shift”
Tweet Tweet Share Share Last Updated on August 23, 2022 Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset. This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at …
Read more “How to Calculate Precision, Recall, F1, and More for Deep Learning Models”
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
Understanding the building blocks and design choices of graph neural networks.
After five years, Distill will be taking a break.
Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.
Reprogramming Neural CA to exhibit novel behaviour, using adversarial attacks.
Tweet Tweet Share Share Last Updated on August 19, 2022 Sponsored Post This is your last chance to sign up for Stefan Krawczyk’s exclusive live cohort, starting next week (August 22nd). We already have students enrolled from Apple, Amazon, Spotify, Nubank, Workfusion, Glassdoor, ServiceNow, and more. Stefan Krawczky has spent the last 15+ years …
Read more “Last call: Stefan Krawcyzk’s ‘Mastering MLOps’ Live Cohort”
Tweet Tweet Share Share Last Updated on August 15, 2022 The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand …
Read more “Why Initialize a Neural Network with Random Weights?”