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Found 5 papers, 4 papers with code
Estimating Q(s,s') with Deterministic Dynamics Gradients
In this paper, we introduce a novel form of a value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
Metropolis-Hastings Generative Adversarial Networks
We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs.
Applying Faster R-CNN for Object Detection on Malaria Images
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data.
