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pro vyhledávání: '"Roy, Anurag"'
Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently, pretrained vision transformers combined with prompt tuning have shown promise for overcoming ca
Externí odkaz:
http://arxiv.org/abs/2403.20317
Autor:
Shammah, Nathan, Roy, Anurag Saha, Almudever, Carmen G., Bourdeauducq, Sébastien, Butko, Anastasiia, Cancelo, Gustavo, Clark, Susan M., Heinsoo, Johannes, Henriet, Loïc, Huang, Gang, Jurczak, Christophe, Kotilahti, Janne, Landra, Alessandro, LaRose, Ryan, Mari, Andrea, Nowrouzi, Kasra, Ockeloen-Korppi, Caspar, Prawiroatmodjo, Guen, Siddiqi, Irfan, Zeng, William J.
Publikováno v:
APL Quantum 1, 011501 (2024)
Quantum technologies such as communications, computing, and sensing offer vast opportunities for advanced research and development. While an open-source ethos currently exists within some quantum technologies, especially in quantum computer programmi
Externí odkaz:
http://arxiv.org/abs/2309.17233
Autor:
Roy, Anurag, Verma, Vinay Kumar, Voonna, Sravan, Ghosh, Kripabandhu, Ghosh, Saptarshi, Das, Abir
Continual Learning (CL) involves training a machine learning model in a sequential manner to learn new information while retaining previously learned tasks without the presence of previous training data. Although there has been significant interest i
Externí odkaz:
http://arxiv.org/abs/2308.11357
Generating natural language questions from visual scenes, known as Visual Question Generation (VQG), has been explored in the recent past where large amounts of meticulously labeled data provide the training corpus. However, in practice, it is not un
Externí odkaz:
http://arxiv.org/abs/2210.07076
We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks required for quantum computing. In the same framework, we perform model-based simulations t
Externí odkaz:
http://arxiv.org/abs/2205.04829
Publikováno v:
In Heliyon 15 February 2024 10(3)
A large fraction of textual data available today contains various types of 'noise', such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and
Externí odkaz:
http://arxiv.org/abs/2101.03303
Autor:
Wittler, Nicolas, Roy, Federico, Pack, Kevin, Werninghaus, Max, Roy, Anurag Saha, Egger, Daniel J., Filipp, Stefan, Wilhelm, Frank K., Machnes, Shai
Publikováno v:
Phys. Rev. Applied 15, 034080 (2021)
Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, the highly detailed system characterization required to understand the underlyi
Externí odkaz:
http://arxiv.org/abs/2009.09866
Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the mode of the documents (e.g., images) from a given training set. Such a
Externí odkaz:
http://arxiv.org/abs/2007.12212
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