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Found 10 papers, 5 papers with code
Unsupervised Neural Machine Translation with Generative Language Models Only
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models.
Autonomous object harvesting using synchronized optoelectronic microrobots
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate.
Exploration by Random Network Distillation
In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.
How to train your MAML
The field of few-shot learning has recently seen substantial advancements.
Variational Option Discovery Algorithms
We explore methods for option discovery based on variational inference and make two algorithmic contributions.
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems.
The Context-Aware Learner
Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario.
Data Augmentation Generative Adversarial Networks
The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items.
Towards a Neural Statistician
We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
Censoring Representations with an Adversary
The flexibility of this method is shown via a novel problem: removing annotations from images, from unaligned training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model.
