5 papers
Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns
Vatsal Baherwani, Zixi Chen, Shikai Qiu +2
Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learn…
Hyperparameter Transfer Enables Consistent Gains of Matrix-Preconditioned Optimizers Across Scales
Shikai Qiu, Zixi Chen, Hoang Phan +2
Several recently introduced deep learning optimizers utilizing matrix-level preconditioning have shown promising speedups relative to the current dominant optimizer AdamW, particul…
Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks
Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson +2
What scaling limits govern neural network training dynamics when model size and training time grow in tandem? We show that despite the complex interactions between architecture, tr…
Out-of-Distribution Detection Methods Answer the Wrong Questions
Yucen Lily Li, Daohan Lu, Polina Kirichenko +4
To detect distribution shifts and improve model safety, many out-of-distribution (OOD) detection methods rely on the predictive uncertainty or features of supervised models trained…
Controllable Prompt Tuning For Balancing Group Distributional Robustness
Hoang Phan, Andrew Gordon Wilson, Qi Lei
Models trained on data composed of different groups or domains can suffer from severe performance degradation under distribution shifts. While recent methods have largely focused o…