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Wasserstein Exponential Kernels

, ,  - 2020

In the context of kernel methods, the similarity between data points is encoded by the kernel function which is often defined thanks to the Euclidean distance, a common example being the squared exponential kernel. Recently, other distances relying on optimal transport theory - such as the Wasserstein distance between probability distributions - ...


Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

, ,  - 2020

CT scanners that are commonly-used in hospitals nowadays produce low-resolution images, up to 512 pixels in size. One pixel in the image corresponds to a one millimeter piece of tissue. In order to accurately segment tumors and make treatment plans, doctors need CT scans of higher resolution. The same problem appears in MRI. In this paper, we pro...


Bayesian Semi-supervised learning under nonparanormality

,  - 2020

Semi-supervised learning is a classification method which makes use of both labeled data and unlabeled data for training. In this paper, we propose a semi-supervised learning algorithm using a Bayesian semi-supervised model. We make a general assumption that the observations will follow two multivariate normal distributions depending on their tru...


Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning

 - 2020

This study proposes a novel biologically-motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and back propagation (BP) learning is the most powerful method in recent machine learning regimes. However, it requires large labeled data for training, and this requirement can occasionally become a barrier fo...


Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network

, , , , ,  - 2020

We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recentl...


Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network

, , , ,  - 2020

Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop an effective spatiotemporal feature alignment network tailored to VSP, mainly including two key sub-networks: a multi-scale deformable con...


Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure

, ,  - 2020

Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to impro...


Learning Diverse Features with Part-Level Resolution for Person Re-Identification

, , , ,  - 2020

Learning diverse features is key to the success of person re-identification. Various part-based methods have been extensively proposed for learning local representations, which, however, are still inferior to the best-performing methods for person re-identification. This paper proposes to construct a strong lightweight network architecture, terme...


Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion

, , , ,  - 2020

This paper describes the solution of the POLINKS team to the RecSys Challenge 2019 that focuses on the task of predicting the last click-out in a session-based interaction. We propose an ensemble approach comprising a matrix factorization for modeling the interaction user-item, and a session-aware learning model implemented with a recurrent neura...


Continual Local Replacement for Few-shot Image Recognition

, , , ,  - 2020

The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few labeled data could not accurately represent the true data distribution. In this work, we use a sophisticated network...