2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

MLSP-48: Neural Network Applications

Session Type: Poster
Time: Friday, 11 June, 14:00 - 14:45
Location: Gather.Town
Session Chair: Yonghee Han, Qualcomm
 
MLSP-48.1: TASK-AWARE NEURAL ARCHITECTURE SEARCH
         Cat Le; Duke University
         Mohammadreza Soltani; Duke University
         Robert Ravier; Duke University
         Vahid Tarokh; Duke University
 
MLSP-48.2: F-NET: FUSION NEURAL NETWORK FOR VEHICLE TRAJECTORY PREDICTION IN AUTONOMOUS DRIVING
         Jue Wang; Peking University / Tencent Technology (Beijing) Company Limited
         Ping Wang; Peking University
         Chao Zhang; Peking University
         Kuifeng Su; Tencent Company
         Jun Li; University of Chinese Academy of Sciences
 
MLSP-48.3: UNSUPERVISED RECONSTRUCTION OF SEA SURFACE CURRENTS FROM AIS MARITIME TRAFFIC DATA USING LEARNABLE VARIATIONAL MODELS
         Simon Benaïchouche; IMT Atlantique
         Clement Le Goff; Eodyn
         Yann Guichoux; Eodyn
         François Rousseau; IMT Atlantique
         Ronan Fablet; IMT Atlantique
 
MLSP-48.4: A COMPACT JOINT DISTILLATION NETWORK FOR VISUAL FOOD RECOGNITION
         Heng Zhao; Nanyang Technological University
         Kim-Hui Yap; Nanyang Technological University
         Alex Chichung Kot; Nanyang Technological University
 
MLSP-48.5: PIPELINE SAFETY EARLY WARNING METHOD FOR DISTRIBUTED SIGNAL USING BILINEAR CNN AND LIGHTGBM
         Yiyuan Yang; Tsinghua University
         Yi Li; Tsinghua University
         Haifeng Zhang; Tsinghua University
 
MLSP-48.6: DEEP LEARNING BASED HYBRID PRECODING IN DUAL-BAND COMMUNICATION SYSTEMS
         Rafail Ismayilov; Fraunhofer Heinrich-Hertz-Institut
         Renato L. G. Cavalcante; Fraunhofer Heinrich-Hertz-Institut
         Sławomir Stańczak; Fraunhofer Heinrich-Hertz-Institut
 
MLSP-48.7: DEEP LEARNING-BASED CROSS-LAYER RESOURCE ALLOCATION FOR WIRED COMMUNICATION SYSTEMS
         Pourya Behmandpoor; KU Leuven
         Jeroen Verdyck; KU Leuven
         Marc Moonen; KU Leuven