986 citations · 2k across the 50 of their papers we have counts for
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Mixed Membership Recurrent Neural Networks
Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres +2
Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for…
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…
Towards Explainable Deep Learning for Credit Lending: A Case Study
Ceena Modarres, Mark Ibrahim, Melissa Louie +1
Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predic…
A Deep Tree-Structured Fusion Model for Single Image Deraining
Xueyang Fu, Qi Qi, Yue Huang +3
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a rel…
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Liyan Sun, Jiexiang Wang, Yue Huang +3
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervise…
Fully Supervised Speaker Diarization
Aonan Zhang, Quan Wang, Zhenyao Zhu +2
In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discrimina…
MBA: Mini-Batch AUC Optimization
San Gultekin, Avishek Saha, Adwait Ratnaparkhi +1
Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for o…
Lightweight Pyramid Networks for Image Deraining
Xueyang Fu, Borong Liang, Yue Huang +2
Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential applica…
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
Liyan Sun, Zhiwen Fan, Yue Huang +2
The need for fast acquisition and automatic analysis of MRI data is growing in the age of big data. Although compressed sensing magnetic resonance imaging (CS-MRI) has been studied…
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
Delu Zeng, Yixuan He, Li Liu +4
Although deep CNNs have brought significant improvement to image saliency detection, most CNN based models are sensitive to distortion such as compression and noise. In this paper,…
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
Liyan Sun, Zhiwen Fan, Yue Huang +2
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimizat…
A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Zhiwen Fan, Liyan Sun, Xinghao Ding +3
Compressed sensing MRI is a classic inverse problem in the field of computational imaging, accelerating the MR imaging by measuring less k-space data. The deep neural network model…
A Divide-and-Conquer Approach to Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Xinghao Ding +3
Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In…
A Deep Error Correction Network for Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Yue Huang +2
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize…