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07/17/2023
An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
In the realm of out-of-distribution generalization tasks, finetuning has...
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06/29/2023
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
We introduce the first large-scale dataset, MNISQ, for both the Quantum ...
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03/07/2023
Sketch-based Medical Image Retrieval
The amount of medical images stored in hospitals is increasing faster th...
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02/20/2023
Nystrom Method for Accurate and Scalable Implicit Differentiation
The essential difficulty of gradient-based bilevel optimization using im...
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11/15/2022
Will Large-scale Generative Models Corrupt Future Datasets?
Recently proposed large-scale text-to-image generative models such as DA...
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03/23/2021
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval
In medical imaging, the characteristics purely derived from a disease sh...
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02/09/2021
Graph Energy-based Model for Substructure Preserving Molecular Design
It is common practice for chemists to search chemical databases based on...
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11/12/2020
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
Medical images can be decomposed into normal and abnormal features, whic...
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06/14/2020
Meta Approach to Data Augmentation Optimization
Data augmentation policies drastically improve the performance of image ...
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05/26/2020
Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks
Recent advances in deep learning have facilitated near-expert medical im...
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11/16/2019
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Data augmentation methods are indispensable heuristics to boost the perf...
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04/17/2019
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Prostate cancer is the most common malignant tumors in men but prostate ...
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03/29/2019
