Emergency adaptation to distance learning: no magic bullet, but where there’s a will, there’s a way
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We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy cons ...
Accepted to the International Conference on Engineering Education 18-22 August 2002, Manchester, UK Introducing Flexibility in Traditional Engineering Education by Providing Dedicated Online Experimentation and Tutoring Resources Sylvie Ursulet and Denis G ...
We use different criteria to judge teaching methods and learning environments as researchers and teachers. As researchers, we tend to rely on learning gains measured in controlled conditions. As teacher, the skilled management of classroom constraints resu ...
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The ...
In the framework of indoor mobile robotics, place recognition is a challenging task, where it is crucial that self-localization be enforced precisely, notwithstanding the changing conditions of illumination, objects being shifted around and/or people affec ...
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The ...
Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...
We propose a fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques. We show that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is ...
Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...
We present an algorithm enabling a humanoid robot to visually learn its body schema, knowing only the number of degrees of freedom in each limb. By “body schema” we mean the joint positions and orientations and thus the kinematic function. The learning is ...