Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their
core is an attention function which models pairwise interactions between the inputs at every …

Combining satellite imagery with machine learning (SIML) has the potential to address
global challenges by remotely estimating socioeconomic and environmental conditions in …

Kernels are powerful and versatile tools in machine learning and statistics. Although the
notion of universal kernels and characteristic kernels has been studied, kernel selection still …

Deep neural networks successfully pervaded many applications domains and are
increasingly used in critical decision processes. Understanding their workings is desirable …

Neural Linear Models (NLM) are deep Bayesian models that produce predictive
uncertainties by learning features from the data and then performing Bayesian linear …

We propose a new, more actionable view of neural network interpretability and data analysis
by leveraging the remarkable matching effectiveness of representations derived from deep …

Matrix factorization is at the heart of many machine learning algorithms, for example,
dimensionality reduction (eg kernel PCA) or recommender systems relying on collaborative …

In recent years, deep neural networks have revolutionized many application domains of
machine learning and are key components of many critical decision or predictive processes …

Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainty
by learning features from the data and then performing Bayesian linear regression over …

Neural Linear Models (NLMs) are deep Bayesian machine learning models that appear in a
variety of contexts due to their data adaptivity and model flexibility, including many settings …