1 code implementation • 7 Dec 2023 • Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, Madian Khabsa
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
no code implementations • 6 Jul 2023 • Mokshagna Sai Teja Karanam, Tushar Kataria, Krithika Iyer, Shireen Elhabian
However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models.
1 code implementation • 13 May 2023 • Krithika Iyer, Shireen Elhabian
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population.
no code implementations • 6 Sep 2022 • Krithika Iyer, Alan Morris, Brian Zenger, Karthik Karanth, Benjamin A Orkild, Oleksandre Korshak, Shireen Elhabian
This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population.
no code implementations • 25 May 2022 • Krithika Iyer, Riddhish Bhalodia, Shireen Elhabian
With extensive experimentation on synthetic and public image datasets, we show that the proposed model learns the relevant latent bottleneck dimensionality without compromising the representation and generation quality of the samples.