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Found 9 papers, 3 papers with code
Towards Multiple Enhancement Styles Generation in Mammography
The presented DL framework is denoted as DL-ME for simplicity.
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers
Moreover, several attentions are learned by different heads, and each head of the MHSA layer encodes the semantic context-aware representation to obtain discriminative features.
Radon Inversion via Deep Learning
Qualitative results show promising reconstruction performance of the iRadonMap.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces.
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
Recurrent Reinforcement Learning: A Hybrid Approach
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.
