This paper proposes DRL-ED-TSPP, a deep reinforcement learning (DRL) model with an Encoder-Decoder architecture, to solve the Traveling Salesman Problem with Profits (TSPP) for sustainable cultural ...

This article extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder–decoder neural networks can be tasked not only to predict but also to yield a ...

The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual information across ...

In this paper we frame the interpolation problem as a self-learning task using a deep encoder-decoder network. We compare our approach against contemporary interpolation methods on a publicly ...

Our proposed method accomplishes the feature engineering on higher-level learning features; this also ensures encoder and decoder for constructing the generative units. The experimental outcome of our ...

Decoder guest host Jon Fortt and Cassie Kozyrkov, Google’s former chief decision scientist, on the role AI will play in ...

Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

In this letter, a model-driven deep learning (DL) decoder for irregular binary low-density parity-check (LDPC) codes is proposed via the alternating direction method of multipliers (ADMM) technique.

End-to-end (E2E) models, including the attention-based encoder-decoder (AED) models, have achieved promising performance on the automatic speech recognition (ASR) task. However, the supervised ...

Furthermore, the proposed decoder outperforms convolutional neural network and deep neural network-based decoders, validating its superiority in decoding polar codes for short packet transmission in ...