Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Upadhyay, Uddeshya"'
Autor:
Upadhyay, Uddeshya, Bade, Sairam, Puranik, Arjun, Asfahan, Shahir, Babu, Melwin, Lopez-Jimenez, Francisco, Asirvatham, Samuel J., Prasad, Ashim, Rajasekharan, Ajit, Awasthi, Samir, Barve, Rakesh
Publikováno v:
Transactions on Machine Learning Research (TMLR), 2023
The automated analysis of medical time series, such as the electrocardiogram (ECG), electroencephalogram (EEG), pulse oximetry, etc, has the potential to serve as a valuable tool for diagnostic decisions, allowing for remote monitoring of patients an
Externí odkaz:
http://arxiv.org/abs/2311.13821
Large-scale vision-language models (VLMs) like CLIP successfully find correspondences between images and text. Through the standard deterministic mapping process, an image or a text sample is mapped to a single vector in the embedding space. This is
Externí odkaz:
http://arxiv.org/abs/2307.00398
Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation and labeling makes it challenging to achieve accurate results. We propose
Externí odkaz:
http://arxiv.org/abs/2305.17520
Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and autonomous systems. However, accurately quantifying uncertainty remai
Externí odkaz:
http://arxiv.org/abs/2302.11012
Autor:
Aggarwal, Aditya, Gairola, Siddhartha, Upadhyay, Uddeshya, Vasishta, Akshay P, Rao, Diwakar, Goyal, Aditya, Murali, Kaushik, Kwatra, Nipun, Jain, Mohit
Refractive error is the most common eye disorder and is the key cause behind correctable visual impairment, responsible for nearly 80% of the visual impairment in the US. Refractive error can be diagnosed using multiple methods, including subjective
Externí odkaz:
http://arxiv.org/abs/2208.05552
High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning techniques allow uncertainty estimation, training them with large-sca
Externí odkaz:
http://arxiv.org/abs/2207.06873
When do gradient-based explanation algorithms provide perceptually-aligned explanations? We propose a criterion: the feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we intr
Externí odkaz:
http://arxiv.org/abs/2206.07387
Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty, lead
Externí odkaz:
http://arxiv.org/abs/2110.12467
Image-to-image translation is an ill-posed problem as unique one-to-one mapping may not exist between the source and target images. Learning-based methods proposed in this context often evaluate the performance on test data that is similar to the tra
Externí odkaz:
http://arxiv.org/abs/2110.03343
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or ac
Externí odkaz:
http://arxiv.org/abs/2107.09892