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Found 13 papers, 10 papers with code
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (MTL).
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels.
Data Augmentations in Deep Weight Spaces
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization.
GD-VDM: Generated Depth for better Diffusion-based Video Generation
GD-VDM is based on a two-phase generation process involving generating depth videos followed by a novel diffusion Vid2Vid model that generates a coherent real-world video.
Guided Deep Kernel Learning
Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL).
Equivariant Architectures for Learning in Deep Weight Spaces
Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction.
Communication Efficient Distributed Learning over Wireless Channels
Vertical distributed learning exploits the local features collected by multiple learning workers to form a better global model.
Functional Ensemble Distillation
Bayesian models have many desirable properties, most notable is their ability to generalize from limited data and to properly estimate the uncertainty in their predictions.
Multi-Task Learning as a Bargaining Game
In this paper, we propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.
Ranked #1 on
Multi-Task Learning
on Cityscapes test
Personalized Federated Learning with Gaussian Processes
A key challenge in this setting is to learn effectively across clients even though each client has unique data that is often limited in size.
Ranked #1 on
Personalized Federated Learning
on CIFAR-100
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
As a result, our method scales well with both the number of classes and data size.
Auxiliary Learning by Implicit Differentiation
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data.
