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Optimizing strains in Metabolic Engineering: comparative analysis of β-Conditional Variational Auto-encoder and Probabilistic PCA for synthetic data generation

Kirbeyi, Doruk (author)

This research explores the landscape of dataset generation through the lens of Probabilistic Principal Component Analysis (PPCA) and β-Conditional Variational Auto-encoder (β-CVAE) models. We conduct a comparative analysis of their respective capabilities in reproducing datasets that mirror the distribution of the original data that comes from a...

bachelor thesis 2024

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Comparative assessment of generative models for transformer- and consumer-level load profiles generation

Xia, W. (author), Huang, Hanyue (author), Duque, Edgar Mauricio Salazar (author), Shengren, H. (author), Palensky, P. (author), Vergara Barrios, P.P. (author)

Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating...

journal article 2024

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Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Song, Yanjie (author), Ou, Junwei (author), Pedrycz, Witold (author), Suganthan, Ponnuthurai Nagaratnam (author), Wang, X. (author), Xing, Lining (author), Zhang, Yue (author)

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem ...

journal article 2024

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Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature Review

Cambaz, Doga (author), Zhang, X. (author)

The recent emergence of LLM-based code generation models can potentially transform programming education. To pinpoint the current state of research on using LLM-based code generators to support the teaching and learning of programming, we conducted a systematic literature review of 21 papers published since 2018. The review focuses on (1) the...

conference paper 2024

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Music·Wave: Companion Product for Orthopedic Surgery Patients with AI -Generated Music

Wang, Ziyi (author)

Orthopedic surgeries are identified as one of the most noisy surgeries inside the OR (operation room). The highest noise sound level could reach up to around 130 dB, which is harmful to patients’ well-being and are likely to evoke negative feelings during surgery. In the previous study investigating the emotional experience of patients who...

master thesis 2023

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Training diffusion models with federated learning: A communication-efficient model for cross-silo federated image generation

de Goede, Matthijs (author)

The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative...

bachelor thesis 2023

document

Use of AI-driven code generation models in teaching and learning programming: a systematic literature review

Cambaz, Doga (author)

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and...

bachelor thesis 2023

document

Controlling Poisson Flow Generative Model: Implementing a class conditional generative model

GEORGIADES, IOANNIS (author)

With the following paper we are planning to present and explore the possibilities of the the newly introduced Poisson Flow Generative Model (PFGM). More specifically, this work aims to introduce the Conditional Poisson Flow Generative Model (CoPFGM), which by extending the existing repository of the PFGM, it will be able to be trained in a way...

bachelor thesis 2023

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End-to-End Federated Diffusion Generative Models for Tabular Data

Xu, Jiaming (author)

Tabular data is widely used in various fields and applications, making the synthesis of such data an active area of research. One important aspect of this research is the development of methods for privacy-preserving data synthesis, which aims to generate synthetic data that retains statistical properties while protecting the privacy of...

master thesis 2023

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Modeling Neuronal Activity with Quantum Generative Adversarial Networks

Fonseca Hernandes, V. (author), Greplová, E. (author)

Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring...

conference paper 2023

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Teacher-apprentices RL (TARL): leveraging complex policy distribution through generative adversarial hypernetwork in reinforcement learning

Tang, Shi Yuan (author), Irissappane, Athirai A. (author), Oliehoek, F.A. (author), Zhang, Jie (author)

Typically, a Reinforcement Learning (RL) algorithm focuses in learning a single deployable policy as the end product. Depending on the initialization methods and seed randomization, learning a single policy could possibly leads to convergence to different local optima across different runs, especially when the algorithm is sensitive to hyper...

journal article 2023

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Extreme precipitation nowcasting using deep generative model

Bi, Haoran (author)

Extreme precipitation can often cause serious hazards such as flooding and landslide. Both pose a threat to human lives and lead to substantial economic loss. It is crucial to develop a reliable weather forecasting system that can predict such extreme events to mitigate the effect of heavy precipitation and increase resilience to these hazards....

master thesis 2022

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A goal-oriented adjoint-based optimization approach to model vortex generator flows using smooth source term distributions

Sahu, Sidhartha (author)

Vortex generators (VGs) are typically about two orders of magnitude smaller than their host component (such as an airplane wing). For this reason, conducting a fully-resolved RANS simulation to isolate their impact on the flow field is computationally expensive. This work presents a goal-oriented adjoint-based approach to model vortex generators...

master thesis 2022

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Generating multivariate load states using a conditional variational autoencoder

Wang, C. (author), Sharifnia, E. (author), Gao, Zhi (author), Tindemans, Simon H. (author), Palensky, P. (author)

For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a...

journal article 2022

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Generating Contextual Load Profiles Using a Conditional Variational Autoencoder

Wang, C. (author), Tindemans, Simon H. (author), Palensky, P. (author)

Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the...

conference paper 2022

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Collective test: Can generative tools working autonomously create new cultures in the long run?

Petrova, Leticija (author), Maeda, Haruka (author)

The term AI has entered the creative industry in the last decades. Computer based design is now omnipresent in everyday life. Will AI take over the creative industry? Can they autonomously be creative? These are some of the headlines propagated by the mainstream media. This paper will unveil the human labor behind these computer generated...

student report 2021

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Discrete state-space active inference in nonstationary environments

Pancham, Naresh (author)

Active inference is a neuroscientific theory, which states that all living systems (e.g. the human brain) minimize a quantity termed the free energy. By minimizing this free energy, living systems keep an accurate representation of the world in their internal model (learning), are provided with an optimal way of acting on the world (action...

master thesis 2021

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Hierarchical Active Inference for Continuous Robot Control: Goal-Directed Hierarchical Models for Active Inference Agents

van Vucht, Victor (author)

Active inference is a method for state estimation and control actions that is based on the Free Energy principle, which explains how biological agents infer the state of their environment and act upon it by maintaining a model of that environment and evaluating predictions. This method merges both action and sensory processing and is therefore a...

master thesis 2021

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Discrete state-space active inference and unknown controlled state transitions

van Roessel, L. (author)

Active inference is a process theory arising from neuroscience which casts perception, action, planning and learning under one optimisation criterion: minimisation of free energy. Current literature on the implementation of discrete state-space active inference focuses on scalability, the comparison to reinforcement learning and its performance...

master thesis 2020

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Predictive and generative machine learning models for photonic crystals

Christensen, Thomas (author), Loh, Charlotte (author), Picek, S. (author), Jakobović, Domagoj (author), Jing, Li (author), Fisher, Sophie (author), Ceperic, Vladimir (author), Joannopoulos, John D. (author), Soljačić, Marin (author)

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods...

journal article 2020