Machine learning application in modelling marine and coastal phenomena: a critical review
A Pourzangbar, M Jalali, M Brocchini - Frontiers in Environmental …, 2023 - frontiersin.org
This study provides an extensive review of over 200 journal papers focusing on Machine
Learning (ML) algorithms' use for promoting a sustainable management of the marine and …
Learning (ML) algorithms' use for promoting a sustainable management of the marine and …
Robustness with query-efficient adversarial attack using reinforcement learning
A measure of robustness against naturally occurring distortions is key to safety, success, and
trustworthiness of machine learning models on deployment. We propose an adversarial …
trustworthiness of machine learning models on deployment. We propose an adversarial …
Rl-cam: Visual explanations for convolutional networks using reinforcement learning
Abstract Convolutional Neural Networks (CNNs) are state-of-the-art models for computer
vision tasks such as image classification, object detection, and segmentation. However …
vision tasks such as image classification, object detection, and segmentation. However …
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. The RLAB framework allows users to customize …
robustness of image classification models. The RLAB framework allows users to customize …
Reinforcement learning based black-box adversarial attack for robustness improvement
We propose a Reinforcement Learning (RL) based adversarial Black-box attack (RLAB) that
aims at adding minimum distortion to the input iteratively to deceive image classification …
aims at adding minimum distortion to the input iteratively to deceive image classification …
[PDF][PDF] Robustness with Black-Box Adversarial Attack using Reinforcement Learning.
A measure of robustness against naturally occurring distortions is key to the safety, success,
and trustworthiness of machine learning models on deployment. We investigate an …
and trustworthiness of machine learning models on deployment. We investigate an …
Function approximation for reinforcement learning controller for energy from spread waves
S Sarkar, V Gundecha, S Ghorbanpour… - arXiv preprint arXiv …, 2024 - arxiv.org
The industrial multi-generator Wave Energy Converters (WEC) must handle multiple
simultaneous waves coming from different directions called spread waves. These complex …
simultaneous waves coming from different directions called spread waves. These complex …
Rtdk-bo: High dimensional bayesian optimization with reinforced transformer deep kernels
Bayesian Optimization (BO), guided by Gaussian process (GP) surrogates, has proven to be
an invaluable technique for efficient, high-dimensional, black-box optimization, a critical …
an invaluable technique for efficient, high-dimensional, black-box optimization, a critical …
Concurrent carbon footprint reduction (c2fr) reinforcement learning approach for sustainable data center digital twin
In recent years, the increasing emphasis on sustainability and carbon footprint reduction has
required the exploration of innovative optimization techniques for data center operators. In …
required the exploration of innovative optimization techniques for data center operators. In …
Measuring robustness with black-box adversarial attack using reinforcement learning
A measure of robustness against naturally occurring distortions is key to the trustworthiness,
safety, and success of machine learning models on deployment. We investigate an …
safety, and success of machine learning models on deployment. We investigate an …