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Found 11 papers, 1 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.
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
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture
In addition, I evaluated the effectiveness of ensembling the last few layers of the network.
