Sorry, you need to enable JavaScript to visit this website.

Sparse Modeling in Image Processing and Deep LearningSparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC).  Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.

Categories:
55 Views

Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for planning - for example when making production capacity or inventory allocation decisions. We propose a practical and robust distribution forecast framework that relies on backtest-based bootstrap and adaptive residual selection.

Categories:
7 Views

This paper describes how semi-supervised learning, called peer collaborative learning (PCL), can be applied to the polyphonic sound event detection (PSED) task, which is one of the tasks in the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. Many deep learning models have been studied to determine what kind of sound events occur where and for how long in a given audio clip.

Categories:
6 Views

In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on core scores of the nodes of a graph through a latent graph structure. Using the proposed model, we jointly infer a sparse graph and nodal core scores that induce dense (sparse) connections in core (respectively, peripheral) parts of the network.

Categories:
5 Views

Predicting the popularity of shopping guide micro-videos incorporating merchandise is crucial for online advertising. What are the significant factors affecting the popularity of the micro-video? How to extract and effectively fuse multiple modalities for the micro-video popularity prediction? This is a question that needs to be urgently answered to better provide insights for advertisers. In this paper, we propose a Multimodal and Temporal Attention Fusion (MTAF) framework to represent and combine multi-modal features.

Categories:
18 Views

Social recommendation (SR) aims to enhance the performance of recommendations by incorporating social information. However, such information is not always reliable, e.g., some of the friends may share similar preferences with the user on a specific item, while others may be irrelevant to this item due to domain differences. Therefore, modeling all of the user's social relationships without considering the relevance of friends will introduce noises to the social context.

Categories:
6 Views

Next basket recommendation aims to provide users a basket of items on the next visit by considering the sequence of their historical baskets. However, since a user's purchase interests vary over time, historical baskets often contain many irrelevant items to his/her next choices. Therefore, it is necessary to denoise the sequence of historical baskets and reserve the indeed relevant items to enhance the recommendation performance.

Categories:
5 Views

Pages