Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms.
Pipeline parallelism enables efficient training of Large Language Models (LLMs) on large-scale distributed accelerator clusters.
In this study, we first reveal that a specific finite-difference computation, composed of both gradient ascent and descent steps, reduces the computational cost of GR.
In general, LPG2vec enables combining predictive power of the most powerful GNNs with the full scope of information encoded in the LPG model, paving the way for neural graph databases, a class of systems where the vast complexity of maintained data will benefit from modern and future graph machine learning methods.
In this work, we reveal that, under specific conditions, NGD with approximate Fisher information achieves the same fast convergence to global minima as exact NGD.
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size.
Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted.
Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size.