arXiv:2206.00255
공식 코드 · Papers with Code
Star algorithm for NN ensembling
원문 초록
Neural network ensembling is a common and robust way to increase model efficiency. In this paper, we propose a new neural network ensemble algorithm based on Audibert's empirical star algorithm. We provide optimal theoretical minimax bound on the excess squared risk. Additionally, we empirically study this algorithm on regression and classification tasks and compare it to most popular ensembling methods.
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Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor
Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
토론
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