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Found 20 papers, 8 papers with code
Evolution through Large Models
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP).
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a, b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
Predictive Complexity Priors
For this reason, we propose predictive complexity priors: a functional prior that is defined by comparing the model's predictions to those of a reference model.
Permutation Equivariant Models for Compositional Generalization in Language
Humans understand novel sentences by composing meanings and roles of core language components.
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects
This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings.
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
Refining the variational posterior through iterative optimization
Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks.
Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models.
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.
Ranked #6 on
Few-Shot Image Classification
on Meta-Dataset Rank
Probabilistic Neural Architecture Search
We showcase the advantages of our approach in applications to CIFAR-10 and ImageNet, where our approach outperforms methods with double its computational cost and matches the performance of methods with costs that are three orders of magnitude larger.
Meta-Learning Probabilistic Inference For Prediction
2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass.
Structured Generation of Technical Reading Lists
Learners need to find suitable documents to read and prioritize them in an appropriate order.
An Investigation into the Pedagogical Features of Documents
We refer to this learning utility as the "pedagogical value" of the document to the learner.
Bayesian Semisupervised Learning with Deep Generative Models
However, these techniques a) cannot account for model uncertainty in the estimation of the model's discriminative component and b) lack flexibility to capture complex stochastic patterns in the label generation process.
