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pro vyhledávání: '"P. Gain ."'
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and
Externí odkaz:
http://arxiv.org/abs/2409.13527
In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies \cite{li-etal-2020-multi-encoder} have shown that the context encoder generates noise and makes the mo
Externí odkaz:
http://arxiv.org/abs/2407.03076
Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training t
Externí odkaz:
http://arxiv.org/abs/2406.02000
The last twenty years have seen the development and popularity of network measurement infrastructures. Internet measurement platforms have become common and have demonstrated their relevance in Internet understanding and security observation. However
Externí odkaz:
http://arxiv.org/abs/2405.04036
Automatic grading and feedback have been long studied using traditional machine learning and deep learning techniques using language models. With the recent accessibility to high performing large language models (LLMs) like LLaMA-2, there is an oppor
Externí odkaz:
http://arxiv.org/abs/2405.00602
In this paper, we formulate the colorization problem into a multinomial classification problem and then apply a weighted function to classes. We propose a set of formulas to transform color values into color classes and vice versa. To optimize the cl
Externí odkaz:
http://arxiv.org/abs/2403.11494
Automatic colorization of gray images with objects of different colors and sizes is challenging due to inter- and intra-object color variation and the small area of the main objects due to extensive backgrounds. The learning process often favors domi
Externí odkaz:
http://arxiv.org/abs/2403.01476
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness. Deep Learning based systems are gradually being introduced as automated support for clinical diagnosis. Since he
Externí odkaz:
http://arxiv.org/abs/2312.08193
Autor:
Gain, Baban, Appicharla, Ramakrishna, Chennabasavaraj, Soumya, Garera, Nikesh, Ekbal, Asif, Chelliah, Muthusamy
Community Question-Answering (CQA) portals serve as a valuable tool for helping users within an organization. However, making them accessible to non-English-speaking users continues to be a challenge. Translating questions can broaden the community's
Externí odkaz:
http://arxiv.org/abs/2310.15259
Autor:
Lefeuvre, Hugo, Gain, Gaulthier, Bădoiu, Vlad-Andrei, Dinca, Daniel, Schiller, Vlad-Radu, Raiciu, Costin, Huici, Felipe, Olivier, Pierre
Supporting mainstream applications is fundamental for a new OS to have impact. It is generally achieved by developing a layer of compatibility allowing applications developed for a mainstream OS like Linux to run unmodified on the new OS. Building su
Externí odkaz:
http://arxiv.org/abs/2309.15996