Concept
Space exploration
Related publications (114)
Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...
EPFL2024Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models
Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Thiemo Wambsganss, Wei Jiang
This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...
2024A lunar reconnaissance drone for cooperative exploration and high-resolution mapping of extreme locations
David Rodriguez Martinez, Daniel Tataru, Erik Uythoven, Thomas Pfeiffer
An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, ...
Pergamon-Elsevier Science Ltd2024Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications
Joshua Alexander Harrison Klein
The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...
EPFL2024Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Ksenia Briling, Puck Elisabeth van Gerwen, Yannick Calvino Alonso, Malte Martin Franke
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...
Royal Soc Chemistry2024Quantum-mechanical effects in photoluminescence from thin crystalline gold films
Giulia Tagliabue, Fateme Kiani Shahvandi, Alan Richard Bowman, Theodoros Tsoulos
Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its ...
2024Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"
Giulia Tagliabue, Fateme Kiani Shahvandi, Alan Richard Bowman, Theodoros Tsoulos
This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...
Zenodo2024Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...
2024Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates
Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...
2024Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...
SPRINGER2023