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Found 8 papers, 4 papers with code
Counterfactual Generation with Identifiability Guarantees
In this work, we tackle the domain-varying dependence between the content and the style variables inherent in the counterfactual generation task.
Partial Identifiability for Domain Adaptation
In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
In this work, we formally characterize and justify existing empirical insights and provide theoretical guarantees of MAE.
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems.
Consensus Control for Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters.
On the Effect of Consensus in Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters.
Ensemble Distillation for Robust Model Fusion in Federated Learning
In most of the current training schemes the central model is refined by averaging the parameters of the server model and the updated parameters from the client side.
Extrapolation for Large-batch Training in Deep Learning
Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data.
