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Found 8 papers, 6 papers with code
Improving fine-grained understanding in image-text pre-training
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs.
SemPPL: Predicting pseudo-labels for better contrastive representations
We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations.
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
Reasoning-Modulated Representations
Neural networks leverage robust internal representations in order to generalise.
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.
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Jack the Reader - A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
emoji2vec: Learning Emoji Representations from their Description
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings.
Programming with a Differentiable Forth Interpreter
Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model.
