Summary of Multi-label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples, By Ao Zhou et al.
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samplesby Ao Zhou, Bin Liu, Jin…
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samplesby Ao Zhou, Bin Liu, Jin…
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest…
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Drivingby Xuemin Hu, Pan Chen,…
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulationby Jingyang Huo, Yikai Wang, Xuelin…
Minimax Optimal Fair Classification with Bounded Demographic Disparityby Xianli Zeng, Guang Cheng, Edgar DobribanFirst submitted…
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no…
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generationby Ruikai Cui, Weizhe…
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classificationby Qingyu Wang,…
Beyond Embeddings: The Promise of Visual Table in Visual Reasoningby Yiwu Zhong, Zi-Yuan Hu, Michael…
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learningby Wenzhuo Liu, Fei Zhu, Cheng-Lin LiuFirst…