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Found 9 papers, 2 papers with code
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models.
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols.
Practical Conditional Neural Process Via Tractable Dependent Predictions
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
Attacking Few-Shot Classifiers with Adversarial Support Poisoning
This paper examines the robustness of deployed few-shot meta-learning systems when they are fed an imperceptibly perturbed few-shot dataset, showing that the resulting predictions on test inputs can become worse than chance.
Attacking Few-Shot Classifiers with Adversarial Support Sets
Few-shot learning systems, especially those based on meta-learning, have recently made significant advances, and are now being considered for real world problems in healthcare, personalization, and science.
Marginal Likelihood Gradient for Bayesian Neural Networks
Bayesian learning of neural networks is attractive as it can protecting against over-fitting and provide automatic methods for inferring important hyperparameters by maximizing the marginal probability of the data.
The Gaussian Process Latent Autoregressive Model
Many real-world prediction problems involve modelling the dependencies between multiple different outputs across the input space.
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information
To reflect this, learning is also carried out in the presence of side information.
Combining Variational Continual Learning with FiLM Layers
The standard architecture for continual learning is a multi-headed neural network, which has shared body parameters and task-specific heads.
