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Found 7 papers, 2 papers with code
PCGPT: Procedural Content Generation via Transformers
The paper presents the PCGPT framework, an innovative approach to procedural content generation (PCG) using offline reinforcement learning and transformer networks.
Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions
We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals.
Active Learning of Causal Structures with Deep Reinforcement Learning
We study the problem of experiment design to learn causal structures from interventional data.
Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices
This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices.
cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU
The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data.
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Conclusion: In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices.
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms.
