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20022025
most citedHigh-sensitivity diamond magnetometer with nanoscale resolution

2k citations

Showing 2019Show all

183 papers · 1 filter

cs.IR201930 cited

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Yun He, Jianling Wang, Wei Niu +1

User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images o…

cs.NI2019

Predictive Scheduling for Virtual Reality

I-Hong Hou, Narges Zarnaghi Naghsh, Sibendu Paul +2

A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless netw…

cs.IT20192 cited

Beamforming Learning for mmWave Communication: Theory and Experimental Validation

ohaned Chraiti, Dmitry Chizhik, Jinfeng Du +3

To establish reliable and long-range millimeter-wave (mmWave) communication, beamforming is deemed to be a promising solution. Although beamforming can be done in the digital and a…

cs.LG201951 cited

Practical Solutions for Machine Learning Safety in Autonomous Vehicles

Sina Mohseni, Mandar Pitale, Vasu Singh +1

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to a…

astro-ph.EP201911 cited

OGLE-2013-BLG-0911Lb: A Secondary on the Brown-Dwarf Planet Boundary around an M-dwarf

Shota Miyazaki, Takahiro Sumi, David P. Bennett +92

We present the analysis of the binary-lens microlensing event OGLE-2013-BLG-0911. The best-fit solutions indicate the binary mass ratio of q~0.03 which differs from that reported i…

cs.CV20199 cited

In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation

Ye Yuan, Wuyang Chen, Yang Yang +1

The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…