MLSP-16.2
HOW NEURAL PROCESSES IMPROVE GRAPH LINK PREDICTION
Huidong Liang, Junbin Gao, The University of Sydney, Australia
Session:
Self-supervised Learning Methods II
Track:
Machine Learning for Signal Processing
Location:
Gather Area H
Presentation Time:
Mon, 9 May, 21:00 - 21:45 China Time (UTC +8)
Mon, 9 May, 13:00 - 13:45 UTC
Mon, 9 May, 13:00 - 13:45 UTC
Session Chair:
Subhro Das, IBM Research
Session MLSP-16
MLSP-16.1: SEMI-SUPERVISED GAUSSIAN MIXTURE VARIATIONAL AUTOENCODER FOR PULSE SHAPE DISCRIMINATION
Abdullah Abdulaziz, Yoann Altmann, Stephen McLaughlin, Heriot-Watt University, United Kingdom of Great Britain and Northern Ireland; Jianxin Zhou, Angela Di Fulvio, University of Illinois, United States of America
MLSP-16.2: HOW NEURAL PROCESSES IMPROVE GRAPH LINK PREDICTION
Huidong Liang, Junbin Gao, The University of Sydney, Australia
MLSP-16.3: UNCERTAINTY ESTIMATION WITH A VAE-CLASSIFIER HYBRID MODEL
Shuyu Lin, Niki Trigoni, Stephen Roberts, University of Oxford, United Kingdom of Great Britain and Northern Ireland; Ronald Clark, Imperial College London, United Kingdom of Great Britain and Northern Ireland
MLSP-16.4: CONTEXT-AWARE GRAPH-BASED SELF-SUPERVISED LEARNING OF WHOLE SLIDE IMAGES
Milan Aryal, Nasim Yahyasoltani, Marquette University, United States of America
MLSP-16.5: CONTRASTIVE SENSOR TRANSFORMER FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL ASSETS
Zaharah Bukhsh, Eindhoven University of Technology, Netherlands
MLSP-16.6: IMPROVING ANOMALY DETECTION WITH A SELF-SUPERVISED TASK BASED ON GENERATIVE ADVERSARIAL NETWORK
Heyan Chai, Weijun Su, Siyu Tang, Harbin Institute of Technology (Shenzhen), China; Ye Ding, Dongguan University of Technology, China; Binxing Fang, Qing Liao, Harbin Institute of Technology (Shenzhen), China