no code implementations • 23 Nov 2023 • Uddeshya Upadhyay, Sairam Bade, Arjun Puranik, Shahir Asfahan, Melwin Babu, Francisco Lopez-Jimenez, Samuel J. Asirvatham, Ashim Prasad, Ajit Rajasekharan, Samir Awasthi, Rakesh Barve
To address these challenges, we propose HypUC, a framework for imbalanced probabilistic regression in medical time series, making several contributions.
1 code implementation • ICCV 2023 • Uddeshya Upadhyay, Shyamgopal Karthik, Massimiliano Mancini, Zeynep Akata
We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment in a post-hoc manner without needing large-scale datasets or computing.
no code implementations • 27 May 2023 • Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee
Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc.
no code implementations • 21 Feb 2023 • Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt, Bernhard Schölkopf, Zeynep Akata
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.
no code implementations • 10 Aug 2022 • Aditya Aggarwal, Siddhartha Gairola, Uddeshya Upadhyay, Akshay P Vasishta, Diwakar Rao, Aditya Goyal, Kaushik Murali, Nipun Kwatra, Mohit Jain
We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model.
1 code implementation • 14 Jul 2022 • Uddeshya Upadhyay, Shyamgopal Karthik, Yanbei Chen, Massimiliano Mancini, Zeynep Akata
Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates.
no code implementations • 15 Jun 2022 • Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike Von Luxburg
We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold.
1 code implementation • NeurIPS 2021 • Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs.
no code implementations • 7 Oct 2021 • Uddeshya Upadhyay, Viswanath P. Sudarshan, Suyash P. Awate
Our experiments with two different real-world datasets show that the proposed method (i)~is robust to OOD-noisy test data and provides improved accuracy and (ii)~quantifies voxel-level uncertainty in the predictions.
no code implementations • 21 Jul 2021 • Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan, Zhaolin Chen, Suyash P. Awate
Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions.
no code implementations • 29 Jun 2021 • Uddeshya Upadhyay, Suyash Awate
Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images.
1 code implementation • 29 Jun 2021 • Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata
However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.
1 code implementation • 23 Feb 2021 • Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner.
no code implementations • 9 Dec 2019 • Uddeshya Upadhyay, Nikunj Shah, Sucheta Ravikanti, Mayanka Medhe
Recent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic methods which are based on deep learning based models and back-propagation of gradients to train such models.
no code implementations • 16 Mar 2019 • Uddeshya Upadhyay, Suyash P. Awate
Using loss functions that assume Gaussian-distributed residuals makes the learning sensitive to corruptions in clinical training sets.
no code implementations • 20 Jan 2019 • Uddeshya Upadhyay, Arjun Jain
These variations are known as Batch Effects.