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On the Lower Bound of Minimizing Polyak-Łojasiewicz Functions
Pengyun Yue, Cong Fang, Zhouchen Lin
Polyak-Łojasiewicz (PL) [Polyak, 1963] condition is a weaker condition than the strong convexity but suffices to ensure a global convergence for the Gradient Descent algorithm. In…
Towards Theoretically Inspired Neural Initialization Optimization
Yibo Yang, Hong Wang, Haobo Yuan +1
Automated machine learning has been widely explored to reduce human efforts in designing neural architectures and looking for proper hyperparameters. In the domain of neural initia…
Invertible Rescaling Network and Its Extensions
Mingqing Xiao, Shuxin Zheng, Chang Liu +2
Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, a…
Online Training Through Time for Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with…
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Haotong Yang, Zhouchen Lin, Muhan Zhang
Most knowledge graphs (KGs) are incomplete, which motivates one important research topic on automatically complementing knowledge graphs. However, evaluation of knowledge graph com…
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Zhengyang Shen, Tao Hong, Qi She +2
Steerable models can provide very general and flexible equivariance by formulating equivariance requirements in the language of representation theory and feature fields, which has…