NSL generalizes to Neural Graph Learning [1] as well as to Adversarial Learning [3]. The NSL framework in TensorFlow provides the following easy-to-use APIs and tools for developers to train models ...

Deep neural nets are highly reductive models of the seemingly chaotic electro-chemical transmissions that populate every synapse of our own heads. ... Scrap or inhibit attention mechanisms in the ...

Deep Learning and Neural Networks have found profound applications in both NLP and Computer Vision. Before the Deep Learning era, statistical and Machine Learning techniques were commonly used for ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

Belkin, for example, thinks that even if kernel methods are old theory, they are still not fully understood. His team has shown empirically that kernel methods don’t overfit and do generalize well to ...

Neural network models of perceptual learning. ... For example, the model learns to estimate the horizontal displacement of a single vertical bar relative to an arbitrary reference position, ...

Ksenia Ponomareva and Simone Caenazzo show the feasibility of overcoming the interpretability hurdles around the application of neural networks in the estimation of credit risk for a portfolio of ...

We retrieve examples similar to the target code in a rank-and-filter manner. Given a neural code summarizer, we optimize it into a meta-learner via Model-Agnostic Meta-Learning (MAML). During ...