most citedSustainability of Data Center Digital Twins with Reinforcement Learning

25 citations · 32 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20242 cited

Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning

Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +3

We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classif…

cs.LG20243 cited

A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez +5

There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data…

cs.AI20241 cited

Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves

Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour +5

The industrial multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices in c…

cs.DC202425 cited

Sustainability of Data Center Digital Twins with Reinforcement Learning

Soumyendu Sarkar, Avisek Naug, Antonio Guillen +4

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address t…

cs.CL2023

N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics

Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan +5

We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs thro…

cs.CV2023

Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness

Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +6

We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of d…