output
20162020
most citedTurbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

84 citations

18 papers

cs.LG20207 cited

Multilinear Latent Conditioning for Generating Unseen Attribute Combinations

Markos Georgopoulos, Grigorios Chrysos, Maja Pantic +1

Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images. However, empirical studies have s…

cs.CV20203 cited

Toward fast and accurate human pose estimation via soft-gated skip connections

Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1

This paper is on highly accurate and highly efficient human pose estimation. Recent works based on Fully Convolutional Networks (FCNs) have demonstrated excellent results for this…

eess.SP20201 cited

The Final Frontier: Deep Learning in Space

Vivek Kothari, Edgar Liberis, Nicholas D. Lane

Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a spac…

cs.CV2020

Are Accelerometers for Activity Recognition a Dead-end?

Catherine Tong, Shyam A. Tailor, Nicholas D. Lane

Accelerometer-based (and by extension other inertial sensors) research for Human Activity Recognition (HAR) is a dead-end. This sensor does not offer enough information for us to p…

cs.NI202014 cited

Pioneering Studies on LTE eMBMS: Towards 5G Point-to-Multipoint Transmissions

Hongzhi Chen, De Mi, Manuel Fuentes +6

The first 5G (5th generation wireless systems) New Radio Release-15 was recently completed. However, the specification only considers the use of unicast technologies and the extens…

cs.NI202014 cited

On the Performance of PDCCH in LTE and 5G New Radio

Hongzhi Chen, De Mi, Manuel Fuentes +5

5G New Radio (NR) Release 15 has been specified in June 2018. It introduces numerous changes and potential improvements for physical layer data transmissions, although only point-t…