Summary of Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss, by Kunal Dahiya et al.
Prototypical Extreme Multi-label Classification with a Dynamic Margin Lossby Kunal Dahiya, Diego Ortego, David JiménezFirst…
Prototypical Extreme Multi-label Classification with a Dynamic Margin Lossby Kunal Dahiya, Diego Ortego, David JiménezFirst…
Self-Normalized Resets for Plasticity in Continual Learningby Vivek F. Farias, Adam D. JozefiakFirst submitted to…
On Multi-Stage Loss Dynamics in Neural Networks: Mechanisms of Plateau and Descent Stagesby Zheng-An Chen,…
Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensorsby Wenqiang Chen, Jiaxuan…
Evaluating Neural Networks for Early Maritime Threat Detectionby Dhanush Tella, Chandra Teja Tiriveedhi, Naphtali Rishe,…
Simmering: Sufficient is better than optimal for training neural networksby Irina Babayan, Hazhir Aliahmadi, Greg…
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial…
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothingby Zhichao Wang, Xinhai Chen, Chunye Gong,…
Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep…
Analyzing Neural Network Robustness Using Graph Curvatureby Shuhang Tan, Jayson Sia, Paul Bogdan, Radoslav IvanovFirst…