MA-L.C2: Learning Methodology
Mon, 17 Oct, 16:30 - 18:30 China Standard Time (UTC +8)
Mon, 17 Oct, 10:30 - 12:30 Central European Time (UTC +2)
Mon, 17 Oct, 08:30 - 10:30 UTC
Mon, 17 Oct, 04:30 - 06:30 Eastern Time (UTC -4)
Lecture
Location: Room C2
Session Chair: Charles Deledalle, Brain Corp
Track: Applications of Machine Learning

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