Several organizations have introduced stores that provide public access to pretrained machine learning models and infrastructure. We examine three of them compare the information they against two mobileapp among themselves.

When encountering novel environments, animals perform complex yet structured exploratory behaviors. Despite their typical structuring, the principles underlying exploratory patterns are still not sufficiently understood. Here we analyzed exploratory behavioral data from two modalities: whisking and locomotion in rats and mice. We found that these rodents maximized novelty signal-to-noise ratio ...

The ability to interact with (monitor and steer) a computation while it is running is a practical way of coming to understand it. The size of some computations can overwhelm both the interaction software and the end user. One technique for handling large computations is the use of exploratory visu-alization. It allows users to discover the computation's behavior by providing mechanisms for incr...

models of human category learning is that there is no standardized method for systematically comparing the models' assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects and descriptive validities of a limited number of assumptions at a time. Two example simulatio...

In this paper, we propose a framework based on a subsymbolic approach for the support of exploratory activities in a hyperspace. By using it, it is possible to express the semantic features of the whole hyperspace and the states of exploratory activities in topological order. This approach is applied to generate the navigation information for the exploratory activity. The space explored is chan...

We present our ongoing work on an interactive exploratory information retrieval system designed to explain to the user the reasons for its predictions and help the user to build a mental model to predict how the system will behave. Users indicate which documents interest them and reinforcement learning is used to model the user by allowing the system to trade o↵ between exploration and exploita...

Why do we build models? There are two basic reasons: explanation or prediction [Ripley, 2004]. Using large ensembles of models for prediction is commonplace, but is rarely used for explanation, where we typically choose one “best” model. When there are several equally good models, it is common sense to look at them too, but can the “bad” models tell us something as well? This paper describes ex...