imagenet dataset
MALT Powers Up Adversarial Attacks
Almost Linearity T argeting, based on medium-scale almost linearity assumptions.
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Random Path Selection for Continual Learning
Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
Neural Information Processing Systems http://nips.cc/
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- North America > Canada (0.04)
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Joshua Lee, Prasanna Sattigeri, Gregory Wornell
Neural Information Processing Systems http://nips.cc/
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Position-basedScaledGradientforModel QuantizationandPruning-Appendix
Inthis experiment, we only quantize the weights, not the activations, to compare the performance degradation as weight bit-width decreases. The mean squared errors (MSE) of the weights across different bit-widths are also reported. The name of the layer and the number of parameters in parenthesis are shown in the column. All numbers are results of the last epoch. Table A3: ResNet-32 trained with Adam on the CIFAR-100 dataset.
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Checklist
Figure 1: The thick line in (a) indicates minimal pointsofC.Thethicklinein(b)indicatesweakly minimalpointsofC.
Fast Samplers for Inverse Problems in Iterative Refinement Models
Iterative refinement models, such as diffusion generative models and flow matching methods [Sohl-Dickstein et al., 2015, Ho et al., 2020, Song et al., 2020, Lipman et al., 2023, Albergo and V anden-Eijnden, 2023], have seen increasing popularity in recent months, and much effort has been invested
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- Asia > Middle East > Jordan (0.04)
