Concept
Solomonoff's theory of inductive inference
Related publications (13)
The inductive bias of deep learning: Connecting weights and functions
Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...
EPFL2023Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching
The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...
EPFL2021A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples
Pierre Dillenbourg, Jennifer Kaitlyn Olsen, Louis Pierre Faucon
Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. I ...
ASSOC COMPUTING MACHINERY2020De novo protein design enables the precise induction of RSV-neutralizing antibodies
Luciano Andres Abriata, Bruno Emanuel Ferreira De Sousa Correia, Elisa Oricchio, Sandrine Madeleine Suzanne Georgeon, Jaume Bonet Martinez, Fabian Sesterhenn, Stéphane Rosset, Che Yang, Patricia Corthésy Henrioud, Sabrina Susan Vollers, Elie Charles Eugène Dheilly, Mélanie Evelyne Villard, Yujia Wang
De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remain ...
2020Empirical validation of MesoHABSIM models developed with different habitat suitability criteria for bullhead cottus gobio L. as an indicator species
Giovanni De Cesare, Mikolaj Stanislaw Adamczyk, Piotr Parasiewicz
Application of instream habitat models such as the Mesohabitat Simulation Model (MesoHABSIM) is becoming increasingly popular. Such models can predict alteration to a river physical habitat caused by hydropower operation or river training. They are a tool ...
2019Energy management in manufacturing: From literature review to a conceptual framework
The literature on energy management in manufacturing is rapidly growing. Since the literature is also quite fragmented, we believe that the time has come to delve into current knowledge in the research field to provide directions for policy makers and guid ...
Elsevier2017On recursion-free Horn clauses and Craig interpolation
One of the main challenges in software verification is efficient and precise analysis of programs with procedures and loops. Interpolation methods remain among the most promising techniques for such verification. To accommodate the demands of various progr ...
Springer2015Information content of prior hydrological knowledge in EGU visitors for streamflow prediction
Citizens living in flood-prone areas sometimes outperform hydrological models in terms of flood prediction. An interesting question is whether this is due to more access to information, more prior knowledge or better pattern recognition capabilities. In th ...
2014Message-Passing Algorithms: Reparameterizations and Splittings
The max-product algorithm, a local message-passing scheme that attempts to compute the most probable assignment (MAP) of a given probability distribution, has been successfully employed as a method of approximate inference for applications arising in codin ...
Ieee-Inst Electrical Electronics Engineers Inc2013Sparse Sampling of Signal Innovations
Martin Vetterli, Pina Marziliano, Thierry Blu, Lionel Coulot, Pier Luigi Dragotti
Sparse sampling of continuous-time sparse signals is addressed. In particular, it is shown that sampling at the rate of innovation is possible, in some sense applying Occam's razor to the sampling of sparse signals. The noisy case is analyzed and solved, p ...
Institute of Electrical and Electronics Engineers2008