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Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Adversarial examples have been shown to cause neural networks to fail on a wide range of vision and language tasks, but recent work has claimed that Bayesian neural networks (BNNs) are inherently robust to adversarial perturbations.
Do Efficient Transformers Really Save Computation?
Our results show that while these models are expressive enough to solve general DP tasks, contrary to expectations, they require a model size that scales with the problem size.
Model Collapse Demystified: The Case of Regression
In the era of proliferation of large language and image generation models, the phenomenon of "model collapse" refers to the situation whereby as a model is trained recursively on data generated from previous generations of itself over time, its performance degrades until the model eventually becomes completely useless, i. e the model collapses.
A Tale of Tails: Model Collapse as a Change of Scaling Laws
We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the ''un-learning" of skills, and grokking when mixing human and synthesized data.
Enhancing Certified Robustness of Smoothed Classifiers via Weighted Model Ensembling
Moreover, SWEEN models constructed using a few small models can achieve comparable performance to a single large model with a notable reduction in training time.
Transferred Discrepancy: Quantifying the Difference Between Representations
Our experiments show that TD can provide fine-grained information for varied downstream tasks, and for the models trained from different initializations, the learned features are not the same in terms of downstream-task predictions.
Enhancing Certified Robustness via Smoothed Weighted Ensembling
Moreover, SWEEN models constructed using a few small models can achieve comparable performance to a single large model with a notable reduction in training time.
