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08/11/2023
M M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector
Deep-learning-based object detection methods show promise for improving ...
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03/29/2023
Problems and shortcuts in deep learning for screening mammography
This work reveals undiscovered challenges in the performance and general...
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08/11/2022
Deep is a Luxury We Don't Have
Medical images come in high resolutions. A high resolution is vital for ...
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03/09/2021
Knowledge Evolution in Neural Networks
Deep learning relies on the availability of a large corpus of data (labe...
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03/04/2021
SVMax: A Feature Embedding Regularizer
A neural network regularizer (e.g., weight decay) boosts performance by ...
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07/19/2020
A Generic Visualization Approach for Convolutional Neural Networks
Retrieval networks are essential for searching and indexing. Compared to...
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02/07/2019
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
We introduce an unsupervised formulation to estimate heteroscedastic unc...
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01/24/2019
In Defense of the Triplet Loss for Visual Recognition
We employ triplet loss as a space embedding regularizer to boost classif...
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01/23/2019
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
We cast visual retrieval as a regression problem by posing triplet loss ...
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06/18/2018
Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes
Semantic image segmentation plays an important role in modeling patient-...
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06/16/2018
Two Stream Self-Supervised Learning for Action Recognition
We present a self-supervised approach using spatio-temporal signals betw...
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04/12/2018
QRES: Quantitative Reasoning on Encrypted Security SLAs
While regulators advocate for higher cloud transparency, many Cloud Serv...
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12/23/2017
Texture Synthesis with Recurrent Variational Auto-Encoder
We propose a recurrent variational auto-encoder for texture synthesis. A...
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02/03/2017
