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Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3
Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the mo…
Breaking Neural Network Scaling Laws with Modularity
Akhilan Boopathy, Sunshine Jiang, William Yue +3
Modular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be due to m…
Towards Exact Computation of Inductive Bias
Akhilan Boopathy, William Yue, Jaedong Hwang +2
Much research in machine learning involves finding appropriate inductive biases (e.g. convolutional neural networks, momentum-based optimizers, transformers) to promote generalizat…