Zobrazeno 1 - 10
of 36 879
pro vyhledávání: '"Yan-Yan An"'
Diffusion models (DMs) have demonstrated exceptional generative capabilities across various areas, while they are hindered by slow inference speeds and high computational demands during deployment. The most common way to accelerate DMs involves reduc
Externí odkaz:
http://arxiv.org/abs/2409.03550
Dataset distillation (DD) aims to distill a small, information-rich dataset from a larger one for efficient neural network training. However, existing DD methods struggle with long-tailed datasets, which are prevalent in real-world scenarios. By inve
Externí odkaz:
http://arxiv.org/abs/2408.14506
We extend the parity doublet model for hadronic matter and study the possible presence of quark matter inside the cores of neutron stars with the Nambu-Jona-Lasinio (NJL) model. Considering the uncertainties of the QCD phase diagram and the location
Externí odkaz:
http://arxiv.org/abs/2408.05687
Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT) methods have greatly enhanced language models' mathematical reasoning capabilities, facilitating their integration into instruction tuning datasets with LLMs. However, exist
Externí odkaz:
http://arxiv.org/abs/2408.07089
Autor:
Shi, Rui, Brewer, Michael K., Chan, Carol Yan Yan, Chuss, David T., Couto, Jullianna Denes, Eimer, Joseph R., Karakla, John, Shukawa, Koji, Valle, Deniz A. N., Appel, John W., Bennett, Charles L., Dahal, Sumit, Essinger-Hileman, Thomas, Marriage, Tobias A., Petroff, Matthew A., Rostem, Karwan, Wollack, Edward J.
Front-end polarization modulation enables improved polarization measurement stability by modulating the targeted signal above the low-frequency $1/f$ drifts associated with atmospheric and instrumental instabilities and diminishes the impact of instr
Externí odkaz:
http://arxiv.org/abs/2407.08912
Deep learning has made remarkable progress recently, largely due to the availability of large, well-labeled datasets. However, the training on such datasets elevates costs and computational demands. To address this, various techniques like coreset se
Externí odkaz:
http://arxiv.org/abs/2407.07268
Semi-Supervised Visual Grounding (SSVG) is a new challenge for its sparse labeled data with the need for multimodel understanding. A previous study, RefTeacher, makes the first attempt to tackle this task by adopting the teacher-student framework to
Externí odkaz:
http://arxiv.org/abs/2407.03251
Unlike Object Detection, Visual Grounding task necessitates the detection of an object described by complex free-form language. To simultaneously model such complex semantic and visual representations, recent state-of-the-art studies adopt transforme
Externí odkaz:
http://arxiv.org/abs/2407.03243
Different from Object Detection, Visual Grounding deals with detecting a bounding box for each text-image pair. This one box for each text-image data provides sparse supervision signals. Although previous works achieve impressive results, their passi
Externí odkaz:
http://arxiv.org/abs/2407.03200
In this brief note we calculate the entanglement entropy in $M^{\otimes N}/S_N$ symmetric orbifold CFTs in the presence of topological defects, which were recently constructed in \cite{Gutperle:2024vyp,Knighton:2024noc}. We consider both universal de
Externí odkaz:
http://arxiv.org/abs/2406.10967