Zobrazeno 1 - 10
of 28 455
pro vyhledávání: '"Haider P"'
Large language models excel at creative generation but continue to struggle with the issues of hallucination and bias. While retrieval-augmented generation (RAG) provides a framework for grounding LLMs' responses in accurate and up-to-date informatio
Externí odkaz:
http://arxiv.org/abs/2410.01171
Maintaining numerical stability in machine learning models is crucial for their reliability and performance. One approach to maintain stability of a network layer is to integrate the condition number of the weight matrix as a regularizing term into t
Externí odkaz:
http://arxiv.org/abs/2410.00169
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory o
Externí odkaz:
http://arxiv.org/abs/2409.06805
Autor:
Soliman, Amr, Williams, C, Hopper, Richard, Udrea, Florin, Butt, Haider, Wilkinson, Timothy D.
Mid-infrared (MIR) spectroscopy is a powerful technique employed for a variety of applications, including gas sensing, industrial inspection, astronomy, surveillance, and imaging. Thin-film narrowband interference filters, targeted to specific absorp
Externí odkaz:
http://arxiv.org/abs/2409.05071
We conduct a comprehensive \textit{ab initio} investigation of electron-electron interactions within the pyrochlore structures of R$_2$Ru$_2$O$_7$, R$_2$Ir$_2$O$_7$, Ca$_2$Ru$_2$O$_7$, and Cd$_2$Ru$_2$O$_7$, where R denotes a rare-earth element. Util
Externí odkaz:
http://arxiv.org/abs/2409.01123
Autor:
Volponi, M., Zieliński, J., Rauschendorfer, T., Huck, S., Caravita, R., Auzins, M., Bergmann, B., Burian, P., Brusa, R. S., Camper, A., Castelli, F., Cerchiari, G., Ciuryło, R., Consolati, G., Doser, M., Eliaszuk, K., Giszczak, A., Glöggler, L. T., Graczykowski, Ł., Grosbart, M., Guatieri, F., Gusakova, N., Gustafsson, F., Haider, S., Janik, M. A., Januszek, T., Kasprowicz, G., Khatri, G., Kłosowski, Ł., Kornakov, G., Krumins, V., Lappo, L., Linek, A., Malamant, J., Mariazzi, S., Penasa, L., Petracek, V., Piwiński, M., Pospisil, S., Povolo, L., Prelz, F., Rangwala, S. A., Rawat, B. S., Rienäcker, B., Rodin, V., Røhne, O. M., Sandaker, H., Smolyanskiy, P., Sowiński, T., Tefelski, D., Vafeiadis, T., Welsch, C. P., Wolz, T., Zawada, M., Zurlo, N.
Publikováno v:
Rev. Sci. Instrum. 95, 085116 (2024)
Modern physics experiments are frequently very complex, relying on multiple simultaneous events to happen in order to obtain the desired result. The experiment control system plays a central role in orchestrating the measurement setup: However, its d
Externí odkaz:
http://arxiv.org/abs/2409.01058
Convolutional layers with 1-D filters are often used as frontend to encode audio signals. Unlike fixed time-frequency representations, they can adapt to the local characteristics of input data. However, 1-D filters on raw audio are hard to train and
Externí odkaz:
http://arxiv.org/abs/2408.17358
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and a
Externí odkaz:
http://arxiv.org/abs/2408.13008
Autor:
Hamdani, Syed Jawwad Haider, Saifullah, Saifullah, Agne, Stefan, Dengel, Andreas, Ahmed, Sheraz
Obtaining annotated table structure data for complex tables is a challenging task due to the inherent diversity and complexity of real-world document layouts. The scarcity of publicly available datasets with comprehensive annotations for intricate ta
Externí odkaz:
http://arxiv.org/abs/2408.09800
Autor:
Xue, Yuyang, Yan, Junyu, Dutt, Raman, Haider, Fasih, Liu, Jingshuai, McDonagh, Steven, Tsaftaris, Sotirios A.
Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and
Externí odkaz:
http://arxiv.org/abs/2408.06890