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Found 7 papers, 3 papers with code
Controlling Machine Translation for Multiple Attributes with Additive Interventions
Fine-grained control of machine translation (MT) outputs along multiple attributes is critical for many modern MT applications and is a requirement for gaining users’ trust.
Gradient Sketches for Training Data Attribution and Studying the Loss Landscape
Two important scenarios are training data attribution (tracing a model's behavior to the training data), where one needs to store a gradient for each training example, and the study of the spectrum of the Hessian (to analyze the training dynamics), where one needs to store multiple Hessian vector products.
Cross-Lingual Supervision improves Large Language Models Pre-training
The recent rapid progress in pre-training Large Language Models has relied on using self-supervised language modeling objectives like next token prediction or span corruption.
Scaling Up Influence Functions
We address efficient calculation of influence functions for tracking predictions back to the training data.
Distributed Function Minimization in Apache Spark
We report on an open-source implementation for distributed function minimization on top of Apache Spark by using gradient and quasi-Newton methods.
Learning to Transport with Neural Networks
We compare several approaches to learn an Optimal Map, represented as a neural network, between probability distributions.
Optimality of the final model found via Stochastic Gradient Descent
We study convergence properties of Stochastic Gradient Descent (SGD) for convex objectives without assumptions on smoothness or strict convexity.
