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Found 18 papers, 4 papers with code
A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers.
Sustainability of Data Center Digital Twins with Reinforcement Learning
To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs.
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D).
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide.
N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.
Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness
We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models.
PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability
The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation.
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning
The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach.
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data.
An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification
The uncertainty is estimated by conducting multiple passes of the input through the network to build a distribution; the mean of the standard deviations is reported as the network's uncertainty.
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring.
Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals.
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmia.
Ranked #1 on
Arrhythmia Detection
on MIT-BIH AR
Learning to predict where to look in interactive environments using deep recurrent q-learning
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e. g., sandwich making and playing the video games).
