The negative impact of label noise is well studied in classical supervised learning yet remains an open research question in meta-learning. Meta-learners aim to adapt to unseen tasks by learning a good initial model in meta-training and fine-tuning it to new tasks during meta-testing. In this paper, we present an extensive analysis of the impact of label noise on the performance of meta-learners, specifically gradient-based N-way K-shot learners. We show that the accuracy of Reptile, iMAML, and foMAML drops by up to 34% when meta-training is affected by label noise on the three representative datasets: Omniglot, CifarFS, and MiniImageNet. To strengthen the resilience against label noise, we propose two sampling techniques, namely manifold (Man) and batch manifold (BatMan), which transforms the noisy supervised learners into semi-supervised learners to increase the utility of noisy labels. We construct N-way 2-contrastive-shot tasks through augmentation, learn the embedding via a contrastive loss in meta-training, and perform classification through zeroing on the embeddings in meta-testing. We show that our approach can effectively mitigate the impact of meta-training label noise. Even with 60% wrong labels BatMan and Man can limit the meta-testing accuracy drop to 2.5, 9.4, 1.1% points with existing meta-learners across Omniglot, CifarFS, and MiniImageNet, respectively. We provide our code online: https://gitlab.ewi.tudelft.nl/dmls/publications/batman-clr-noisy-meta-learning.

Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies such as Continual Learning (CL). To enable research reproducibility, we propose a set of experimental best practices that precisely capture and emulate complex learning scenarios. To the best of our knowledge, our framework, Freddie, is the first entirely configurable framework for Federated Continual Learning (FCL), and it can be seamlessly deployed on a large number of machines leveraging containerization and Kubernetes. We demonstrate the effectiveness of Freddie on two use cases, (i) large-scale concurrent FL on CIFAR100 and (ii) heterogeneous task sequence on FCL, which highlight unaddressed performance challenges in FCL scenarios.

Shareable tabular data is of high importance in industry and research. While generating synthetic records is well-studied, research has only recently extended to relational data synthesis. In the tabular generation setting, diffusion and transformer models exhibit superior performance over prior art. However, in the relational setting, diffusion models outperform transformers. This work focuses on the performance gap between tabular transformers and diffusion models in single (tabular) and multi-table (relational) settings, using REaLTabformer and ClavaDDPM as representative state-of-the-art models. We evaluate these architectures on a set of single- and multi-table datasets, highlighting the gap's root causes between the methods. In our experiments, we attribute this difference to the influence of contextual information and data representation. To bridge the gap in the relational setting, we propose two seemingly simple strategies: layer sharing and contextual cues. This work1 offers insights into key design considerations for single- and multitable generative models, including the incorporation of contextual information and the reuse of existing knowledge. With the proposed methods, we achieve improvements of 1.52× and 1.94× for the Logistic Detection and Discriminator Measure metrics, respectively.

In a decade, AI frontier research transitioned from the researcher's workstation to thousands of high-end hardware-accelerated compute nodes. This rapid evolution shows no signs of slowing down in the foreseeable future. While top cloud providers may be able to keep pace with this growth rate, obtaining and efficiently exploiting computing resources at that scale is a daunting challenge for universities and SMEs. This work introduces the Cross-Facility Federated Learning (XFFL) framework to bridge this compute divide, extending the opportunity to efficiently exploit multiple independent data centres for extreme-scale deep learning tasks to data scientists and domain experts. XFFL relies on hybrid workflow abstractions to decouple tasks from environment-specific technicalities, reducing complexity and enhancing reusability. In addition, Federated Learning (FL) algorithms eliminate the need to move large amounts of data between different facilities, reducing time-to-solution and preserving data privacy. The XFFL approach is empirically evaluated by training a full LLaMAv2 7B instance on two facilities of the EuroHPC JU, showing how the increased computing power completely compensates for the additional overhead introduced by two data centres.

This artifact is a guideline how the Edgecaffe framework, presented in [1], can be used. Edgecaffe is an open-source Deep Neural Network framework for efficient multi-network inference on edge devices. This framework enables the layer by layer execution and fine-grained control during inference of Deep Neural Networks. Edgecaffe is created to give more fine grained-control over the execution during inference than offered by the original code of Caffe [2]. Edgecaffe made it possible for Masa to outperform Deepeye [3] and normal bulk execution. Besides the core implementation of Edgecaffe, the repository holds additional tools, Queue Runner and ModelSplitter, that make more convenient to run experiments and prepare newly trained networks