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pro vyhledávání: '"A. Schwinn"'
Autor:
Don Rauf
For many children of the sixties, the gift of a Schwinn was a ticket to freedom, a chance to feel the wind on their face and the steady rotation of rubber at their feet. The Schwinn took many through their childhood adventures, with memories filled o
Autor:
Tanzer, Andrew
Publikováno v:
Forbes. 12/21/1992, Vol. 150 Issue 14, p90-95. 5p. 3 Color Photographs.
Comprehensive evaluation of Large Language Models (LLMs) is an open research problem. Existing evaluations rely on deterministic point estimates generated via greedy decoding. However, we find that deterministic evaluations fail to capture the whole
Externí odkaz:
http://arxiv.org/abs/2410.03523
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:
Schwinn, Leo, Geisler, Simon
Over the past decade, adversarial training has emerged as one of the few reliable methods for enhancing model robustness against adversarial attacks [Szegedy et al., 2014, Madry et al., 2018, Xhonneux et al., 2024], while many alternative approaches
Externí odkaz:
http://arxiv.org/abs/2407.15902
Autor:
Zanca, Dario, Zugarini, Andrea, Dietz, Simon, Altstidl, Thomas R., Ndjeuha, Mark A. Turban, Schwinn, Leo, Eskofier, Bjoern
Understanding human attention is crucial for vision science and AI. While many models exist for free-viewing, less is known about task-driven image exploration. To address this, we introduce CapMIT1003, a dataset with captions and click-contingent im
Externí odkaz:
http://arxiv.org/abs/2408.09948
Existing studies have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Even though Graph Transformers (GTs) surpassed Message-Passing GNNs on several benchmarks, their adversarial robustness properties are unexplored. Ho
Externí odkaz:
http://arxiv.org/abs/2407.11764