Zobrazeno 1 - 10
of 6 082
pro vyhledávání: '"Schrimpf A."'
Autor:
Gokce, Abdulkadir, Schrimpf, Martin
When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition (COR) behaviors and neural response patterns in the primate visual ventral stream (VVS). While recent ma
Externí odkaz:
http://arxiv.org/abs/2411.05712
Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and social inference. In the human brain, neuroscience has identified a core
Externí odkaz:
http://arxiv.org/abs/2411.02280
While today's large language models exhibit impressive abilities in generating human-like text, they require massive amounts of data during training. We here take inspiration from human cognitive development to train models in limited data conditions
Externí odkaz:
http://arxiv.org/abs/2411.00828
Autor:
Schrimpf, Andreas
There are about 6000 stars, that can be seen with the naked eye and have been observed for centuries for various purposes. More modern investigations using advanced telescopes show that our Milky Way, a quite common galaxy, consists of about 100 -- 4
Externí odkaz:
http://arxiv.org/abs/2410.11455
Autor:
Rathi, Neil, Mehrer, Johannes, AlKhamissi, Badr, Binhuraib, Taha, Blauch, Nicholas M., Schrimpf, Martin
Neurons in the brain are spatially organized such that neighbors on tissue often exhibit similar response profiles. In the human language system, experimental studies have observed clusters for syntactic and semantic categories, but the mechanisms un
Externí odkaz:
http://arxiv.org/abs/2410.11516
Large Language Models (LLMs) have been shown to be effective models of the human language system, with some models predicting most explainable variance of brain activity in current datasets. Even in untrained models, the representations induced by ar
Externí odkaz:
http://arxiv.org/abs/2406.15109
Autor:
Tschaikner, Martin, Brandt, Danja, Schmidt, Henning, Bießmann, Felix, Chiaburu, Teodor, Schrimpf, Ilona, Schrimpf, Thomas, Stadel, Alexandra, Haußer, Frank, Beckers, Ingeborg
Publikováno v:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, SPIE 12727 (2023) 1272702
Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This paper presents a multisensor approach that uses AI-based data fusion
Externí odkaz:
http://arxiv.org/abs/2404.18504
Autor:
Brandt, Danja, Tschaikner, Martin, Chiaburu, Teodor, Schmidt, Henning, Schrimpf, Ilona, Stadel, Alexandra, Beckers, Ingeborg E., Haußer, Frank
Publikováno v:
Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer
Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and up-scaled monitoring in order to detect correlations and identify cou
Externí odkaz:
http://arxiv.org/abs/2404.17488
A considerable number of photographic plate archives exist world wide and digitization is in progress or already has been finished. Not only different type of scanners were used but also spatial resolution and dynamic range often were limited due to
Externí odkaz:
http://arxiv.org/abs/2312.02529
This study aims to improve the photometric calibration of astronomical photo plates. The Sonneberg Observatory's sky patrol was selected, comprising about 300,000 plates, and the digitization workflow is implemented using PyPlate. The challenge is to
Externí odkaz:
http://arxiv.org/abs/2312.01453