46 citations · 81 across the 7 of their papers we have counts for
9 papers
Asymptotic behavior of the free interface for entire vector minimizers in phase transitions
Nicholas D. Alikakos, Zhiyuan Geng, Arghir Zarnescu
We study globally bounded entire minimizers of Allen-Cahn systems for potentials with and $W(u)\sim |u-a…
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection
Yuxiang Zhang, Sachin Mehta, Anat Caspi
Semantic segmentation aims to robustly predict coherent class labels for entire regions of an image. It is a scene understanding task that powers real-world applications (e.g., aut…
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
Beibin Li, Ezgi Mercan, Sachin Mehta +5
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three…
EVRNet: Efficient Video Restoration on Edge Devices
Sachin Mehta, Amit Kumar, Fitsum Reda +4
Video transmission applications (e.g., conferencing) are gaining momentum, especially in times of global health pandemic. Video signals are transmitted over lossy channels, resulti…
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Sanjay Subramanian, Lucy Lu Wang, Sachin Mehta +6
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several su…
DeLighT: Deep and Light-weight Transformer
Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer +2
We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. D…
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
Sachin Mehta, Ximing Lu, Donald Weaver +3
Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local represent…
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari +1
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep…
Pyramidal Recurrent Unit for Language Modeling
Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari +1
LSTMs are powerful tools for modeling contextual information, as evidenced by their success at the task of language modeling. However, modeling contexts in very high dimensional sp…