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Found 6 papers, 3 papers with code
Perceptual Grouping in Contrastive Vision-Language Models
In this work we examine how well vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery.
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
The terms multi-task learning and multitasking are easily confused.
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
Amortized Bayesian Meta-Learning
Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data.
Meta-Learning for Semi-Supervised Few-Shot Classification
To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.
Learning the Dimensionality of Word Embeddings
We describe a method for learning word embeddings with data-dependent dimensionality.
