MLSP-24.2
AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS
Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das, IBM, United States of America
Session:
Deep Generative Model
Track:
Machine Learning for Signal Processing
Location:
Gather Area F
Presentation Time:
Tue, 10 May, 21:00 - 21:45 China Time (UTC +8)
Tue, 10 May, 13:00 - 13:45 UTC
Tue, 10 May, 13:00 - 13:45 UTC
Session Chair:
Danilo Comminiello, Sapienza University of Rome
Session MLSP-24
MLSP-24.1: SIMPLER IS BETTER: SPECTRAL REGULARIZATION AND UP-SAMPLING TECHNIQUES FOR VARIATIONAL AUTOENCODERS
Sara Björk, UiT The Arctic University of Norway, Norway; Jonas Nordhaug Myhre, Thomas Haugland Johansen, NORCE Norwegian Research Centre, Norway
MLSP-24.2: AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS
Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das, IBM, United States of America
MLSP-24.3: StyleGAN-induced data-driven regularization for inverse problems
Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
MLSP-24.4: A CLOSER LOOK AT AUTOENCODERS FOR UNSUPERVISED ANOMALY DETECTION
Oyebade Oyedotun, Djamila Aouada, University of Luxembourg, Luxembourg
MLSP-24.5: NFT-K: NON-FUNGIBLE TANGENT KERNELS
Sina Alemohammad, Hossein Babaei, CJ Barberan, Naiming Liu, Lorenzo Luzi, Blake Mason, Richard Baraniuk, Rice University, United States of America
MLSP-24.6: ON DATA AUGMENTATION FOR GAN TRAINING
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, Ngai-Man (Man) Cheung, Singapore University of Technology and Design (SUTD), Singapore