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Found 7 papers, 1 papers with code
Can Generative Models Improve Self-Supervised Representation Learning?
By directly conditioning generative models on a source image representation, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for self-supervised learning.
Random Field Augmentations for Self-Supervised Representation Learning
In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning.
Federated Variational Inference: Towards Improved Personalization and Generalization
We then approximate this process using Variational Inference to train our model efficiently.
BERT for Long Documents: A Case Study of Automated ICD Coding
Transformer models have achieved great success across many NLP problems.
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.
Empirical analysis of representation learning and exploration in neural kernel bandits
We consider policies based on a GP and a Student's t-process (TP).
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization
Neural combinatorial optimization (NCO) aims at designing problem-independent and efficient neural network-based strategies for solving combinatorial problems.
