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One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies

, , ,  - 2018

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In...


Deep Recurrent Q-Learning for Partially Observable MDPs

,  - 2015

Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the fi...


Deep Bayesian Active Learning with Image Data

, ,  - 2017

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in dee...


Deep Learning and Its Applications to Machine Health Monitoring: A Survey

, , , , ,  - 2016

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the wid...


Deep Learning and Its Applications to Machine Health Monitoring: A Survey

, , , , ,  - 2016

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the wid...


Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

, ,  - 2018

Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularizatio...


ChainerMN: Scalable Distributed Deep Learning Framework

, ,  - 2017

One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs. Enabling the use of further computing abilities by distributed processing is essential not only to make the deep learning bigger and faster but also to tackle unsolved challenges. We present the design, imple...


Multi-Objective Deep Reinforcement Learning

, , ,  - 2016

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of...


Deep Learning Face Representation from Predicting 10,000 Classes

, ,  - 2014

This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) ...


Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not

,  - 2016

Deep-learning has dramatically changed the world overnight. It greatly boosted the development of visual perception, object detection, and speech recognition, etc. That was attributed to the multiple convolutional processing layers for abstraction of learning representations from massive data. The advantages of deep convolutional structures in da...