4 citations · 7 across the 7 of their papers we have counts for
7 papers
Verifiable Rewards for Calibrated Probabilistic Forecasting
Sadanand Singh, Allam Reddy, Manan Chopra
Reinforcement learning with verifiable rewards can in principle train calibrated probabilistic forecasters, since a proper scoring rule such as the Brier score is computed from out…
Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces
Sadanand Singh, Allam Reddy, Manan Chopra
We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-f…
Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
Sadanand Singh, Allam Reddy, Manan Chopra
Retrieval-augmented generation (RAG) is routinely extended with methods meant to improve retrieval: query expansion, hierarchical and cross-document summarization, graph-based expa…
Deep is a Luxury We Don't Have
Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette +3
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling…
A Hypersensitive Breast Cancer Detector
Stefano Pedemonte, Brent Mombourquette, Alexis Goh +6
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population…
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
Sadanand Singh, Thomas Paul Matthews, Meet Shah +6
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exa…
A Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images
Thomas P. Matthews, Sadanand Singh, Brent Mombourquette +12
Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multi-site setting for synthetic two-dimensional mammography (…