output
20122025
most citedNetwork Function Virtualization: State-of-the-art and Research Challenges

2k citations

Showing 2016Show all

12 papers · 1 filter

cs.CV2016

Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images

Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars

In this work, we aim at automatically labeling actors in a TV series. Rather than relying on transcripts and subtitles, as has been demonstrated in the past, we show how to achieve…

cs.SI2016★ 1 cited

OSSINT - Open Source Social Network Intelligence An efficient and effective way to uncover "private" information in OSN profiles

Giuseppe Cascavilla, Filipe Beato, Andrea Burattin +2

Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of informatio…

cs.NE2016★ 6 cited

A Differentiable Physics Engine for Deep Learning in Robotics

Jonas Degrave, Michiel Hermans, Joni Dambre +1

An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivati…

cs.CV2016★ 35 cited

Multi-modal dictionary learning for image separation with application in art investigation

Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis +2

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals…

cs.IR2016★ 200 cited

Representation learning for very short texts using weighted word embedding aggregation

Cedric De Boom, Steven Van Canneyt, Thomas Demeester +1

Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic…

cs.LG2016★ 948 cited

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft +2

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples…