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pro vyhledávání: '"A Marten"'
Online controlled experiments, or A/B tests, are large-scale randomized trials in digital environments. This paper investigates the estimands of the difference-in-means estimator in these experiments, focusing on scenarios with repeated measurements
Externí odkaz:
http://arxiv.org/abs/2411.06150
The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in th
Externí odkaz:
http://arxiv.org/abs/2411.03038
The possibility of a Photon-Photon collider extension to the Beam dump of the $17.5$ GeV European XFEL has been discussed before as the first high energy collider of its sort. It would not just be to study the concept of photon colliders but would al
Externí odkaz:
http://arxiv.org/abs/2410.19465
Bubble curtains are widely used to protect marine life from exposure to noise during offshore construction. However, operating a bubble curtain is costly. Therefore optimizing the acoustic effect of the available air is important. An interesting appr
Externí odkaz:
http://arxiv.org/abs/2410.14415
Timing control while preserving determinism is often a key requirement for ensuring the safety and correctness of distributed cyber-physical systems (CPS). Discrete-event (DE) systems provide a suitable model of computation (MoC) for time-sensitive d
Externí odkaz:
http://arxiv.org/abs/2410.06454
In this paper, we study Artinian and Noetherian properties in vector lattices and provide a concrete representation of these spaces. Furthermore, we describe for which Archimedean uniformly complete vector lattices every decreasing sequence of prime
Externí odkaz:
http://arxiv.org/abs/2410.03329
Recent advancements in generative modeling, particularly diffusion models, have opened new directions for time series modeling, achieving state-of-the-art performance in forecasting and synthesis. However, the reliance of diffusion-based models on a
Externí odkaz:
http://arxiv.org/abs/2410.03024
Autor:
Reichlin, Alfredo, Tegnér, Gustaf, Vasco, Miguel, Yin, Hang, Björkman, Mårten, Kragic, Danica
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belong to different tasks concurrently. This is particularly the case in me
Externí odkaz:
http://arxiv.org/abs/2410.01476
Autor:
Fouesneau, Morgan, Momcheva, Ivelina G., Chadayammuri, Urmila, Demianenko, Mariia, Dumont, Antoine, Hviding, Raphael E., Kahle, K. Angelique, Pulatova, Nadiia, Rajpoot, Bhavesh, Scheuck, Marten B., Seeburger, Rhys, Semenov, Dmitry, Villaseñor, Jaime I.
ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields, offering powerful tools for a wide range of applications. These models, commonly trained on vast datasets, exhibit human-like text generation ca
Externí odkaz:
http://arxiv.org/abs/2409.20252
Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks or unpredictable environments, while keeping a transparent policy that is readable and verifiable by humans. We propose the method BEhavior TRee eX
Externí odkaz:
http://arxiv.org/abs/2409.13356