4 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…