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Found 14 papers, 1 papers with code
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
Our results show that the transformer model of moderate depth with gated residual connections around the multi-head attention, multi-layer perceptron, and the transformer block (STrXL) proposed in this paper is optimal and boosts energy efficiency by an average of 22. 1% for these complex spread waves over the existing spring damper (SD) controller.
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.
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels
In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.
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.
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters
Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject.
Automated system to measure Tandem Gait to assess executive functions in children
Furthermore in order to improve the accuracy of the system, a deep learning based model was pre-trained on NTU-RGB+D 120 dataset and then it was fine-tuned on our gait dataset.
A Survey on Contrastive Self-supervised Learning
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets.
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks
Subsequently, results prove superiority of the proposed method over baseline methods for providing a useful representation of videos used in human activity recognition performed on datasets such as KTH, UCF101 and Ball-Drop.
Generative Adversarial Network
Human Activity Recognition
+1
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects.
