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[Submitted on 20 Aug 2024] View a PDF of the paper titled Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models, by Yuanzhe Wang and Alexandre M. Tartakovsky View PDF HTML (experimental) Abstract:We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations and PDE and surrogate models. First, we use the surrogate model to formulate a minimization problem in the reduced space for the maximum a posteriori (MAP) inverse solution. Then,…

[Submitted on 20 Aug 2024] View a PDF of the paper titled BAUST Lipi: A BdSL Dataset with Deep Learning Based Bangla Sign Language Recognition, by Md Hadiuzzaman and 5 other authors View PDF HTML (experimental) Abstract:People commonly communicate in English, Arabic, and Bengali spoken languages through various mediums. However, deaf and hard-of-hearing individuals primarily use body language and sign language to express their needs and achieve independence. Sign language research is burgeoning to enhance communication with the deaf community. While many researchers have made strides in recognizing sign languages such as French, British, Arabic, Turkish, and American, there has…

arXiv:2408.11219v1 Announce Type: new Abstract: Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in their model parameters to learn extensive knowledge compared to larger models. Secondly, high-quality conversational datasets are often scarce, small, and domain-specific. Addressing these challenges, we introduce a novel data distillation framework named CoDi (short for Conversational Distillation, pronounced "Cody"), allowing us to synthesize large-scale, assistant-style datasets in a steerable and diverse manner. Specifically, while our framework is task agnostic at its core, we explore and evaluate the potential of CoDi on the task…