MA-L.C2.2
NON-SMOOTH ENERGY DISSIPATING NETWORKS
Hannah Dröge, Michael Moeller, University of Siegen, Germany; Thomas Möllenhoff, RIKEN Center for AI Project, Japan
Session:
Learning Methodology
Track:
Applications of Machine Learning
Location:
Room C2
Presentation Time:
Mon, 17 Oct, 16:45 - 17:00 China Standard Time (UTC +8)
Mon, 17 Oct, 10:45 - 11:00 Central European Time (UTC +2)
Mon, 17 Oct, 08:45 - 09:00 UTC
Mon, 17 Oct, 04:45 - 05:00 Eastern Time (UTC -4)
Mon, 17 Oct, 10:45 - 11:00 Central European Time (UTC +2)
Mon, 17 Oct, 08:45 - 09:00 UTC
Mon, 17 Oct, 04:45 - 05:00 Eastern Time (UTC -4)
Session Chair:
Charles Deledalle, Brain Corp
Presentation
Discussion
Resources
No resources available.
Session MA-L.C2
MA-L.C2.1: CCL: CLASS-WISE CURRICULUM LEARNING FOR CLASS IMBALANCE PROBLEMS.
Marcos Escudero-Viñolo, Alejandro López-Cifuentes, Universidad Autónoma de Madrid., Spain
MA-L.C2.2: NON-SMOOTH ENERGY DISSIPATING NETWORKS
Hannah Dröge, Michael Moeller, University of Siegen, Germany; Thomas Möllenhoff, RIKEN Center for AI Project, Japan
MA-L.C2.3: DIFFERENTIAL INVARIANTS FOR SE(2)-EQUIVARIANT NETWORKS
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo, Mines ParisTech, France
MA-L.C2.4: EXTRACTING EFFECTIVE SUBNETWORKS WITH GUMBEL-SOFTMAX
Robin Dupont, Sorbonne Université & Netatmo, France; Mohammed Amine Alaoui, Alice Lebois, Netatmo, France; Hichem Sahbi, Sorbonne Université, France
MA-L.C2.5: DEEP METRIC LEARNING-BASED SEMI-SUPERVISED REGRESSION WITH ALTERNATE LEARNING
Adina Zell, Gencer Sumbul, Begüm Demir, Technische Universität Berlin, Germany
MA-L.C2.6: SPATIAL SENSITIVE GRAD-CAM: VISUAL EXPLANATIONS FOR OBJECT DETECTION BY INCORPORATING SPATIAL SENSITIVITY
Toshinori Yamauchi, Masayoshi Ishikawa, Hitachi, Ltd. Research & Development Group, Japan