Unverified50% confidenceFactExact time
Zhang et al. proved in 2017 that standard deep networks can perfectly fit training data with random labels.
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9/2/2026
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UnverifiedExperiments by Zhang et al. (2017) demonstrated that standard deep neural networks can perfectly memorize training data with random labels, meaning that traditional generalization bounds based on model capacity completely break down.80% similarUnverified2017年Zhang等人的论文《Understanding Deep Learning Requires Rethinking Generalization》证明深度网络有能力完美记忆完全随机的标签72% similarUnverifiedGoogle DeepMind's Chinchilla paper optimized the optimal ratio between model parameter count and training data volume.69% similarUnverified2019年Zhu等研究者在NeurIPS会议上发表《Deep Leakage from Gradients》，证明可通过梯度匹配优化重建原始训练图像69% similarUnverifiedIn "On Calibration of Modern Neural Networks" published at ICML in 2017 by Guo et al., the miscalibration phenomenon was empirically demonstrated at scale for the first time, finding that modern deep networks such as ResNet are worse calibrated than earlier shallow networks.66% similar
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