In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers.
Furthermore, this can enable reinforcement learning without rewards, in which the agent learns entirely from these intrinsic sentiment rewards.
Recent advances in Generative Adversarial Networks (GANs) have resulted in its widespread applications to multiple domains.
The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains.
Recent advances in Generative Adversarial Networks facilitated by improvements to the framework and successful application to various problems has resulted in extensions to multiple domains.
Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfer scenarios.
One such approach is Hindsight Experience replay which uses an off-policy Reinforcement Learning algorithm to learn a goal conditioned policy.
Ontological methods are good at encoding Semantic Similarity and Vector Space models are better at encoding Semantic Relatedness.