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Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
We introduce a variational inference model called VIM, for Variational Independent Modules, for sequential data that learns and infers …
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then …
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the …
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art …
Deep ensembles offer consistent performance gains, both in terms of reduced generalization error and improved predictive uncertainty …
This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional …
Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of …
Explainability for machine learning models has gained considerable attention within the research community given the importance of …
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection …