Publications related to Models and Algorithms in Biological Network Evolution with Modularity

Related publications (37)

ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Sogand Salehi, Amir Roshan Zamir, Shuqing Teresa Yeo, Roman Christian Bachmann, Notash Shafiei

Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an individual’s visual preference. Current generative models are, however, tuned to ...

Springer Science and Business Media Deutschland GmbH2025

Automated Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Pascal Fua, Zhen Wei

Traditional aerodynamic shape optimization (ASO) is costly and time-intensive, requiring extensive manual tuning for parameterization to define the design space and manipulate geometries. Conventional parameterization methods limit flexibility and demand s ...

American Institute of Aeronautics and Astronautics (AIAA)2025

SketchAI: A "Sketch-First" Approach to Incorporating Generative AI into Fashion Design

Aleksandra Novikova, Tanja Christina Käser Jacober, Daniel Carnieto Tozadore

Generative AI technologies are increasingly being incorporated into creativity support tools. However, most generative AI tools rely on text-based prompting, requiring users to translate visual ideas into linguistic descriptions. This approach is misaligne ...

ACM2025

Wear across scales

Sacha Zenon Wattel

When two objects are rubbed together, they create friction, heat, and their surfaces might be worn away. However, the fundamental mechanisms behind these processes remain poorly understood. While empirical engineering models exist, they are tailored to spe ...

EPFL2025

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Jérôme Chenal

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Predicting protein interactions using geometric deep learning on protein surfaces

Freyr Sverrisson

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

EPFL2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Reconstruction and analysis of genome-scale metabolic networks in single organisms and microbial communities

Evangelia Vagena

Microorganisms are a key component in the chain of life. They are essential for agriculture, produce a large proportion of oxygen, and play a central role in the cycle of elements. Microorganisms are widely used in the production of food and alcoholic beve ...

EPFL2024

Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

Asli Sahin

Microorganisms are essential for life on Earth, performing key roles in numerous biological processes. Their influence extends across a wide spectrum, from human health and ecological balance to advancements in biotechnology and industrial applications. Th ...

EPFL2024

GELEX: Generative AI-Hybrid System for Example-Based Learning

Aybars Yazici, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Jibril Albachir Frej

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Association for Computing Machinery2024

Fundamental Limits of Prompt Compression: A Rate- Distortion Framework for Black-Box Language Models

Michael Christoph Gastpar, Adway Girish, Ashok Vardhan Makkuva

We formalize the problem of prompt compression for large language models (LLMs) and present a framework to unify token-level prompt compression methods which create hard prompts for black-box models. We derive the distortion-rate function for this setup as ...

Curran Associates, Inc.2024

Network-based kinetic models: Emergence of a statistical description of the graph topology

Matteo Raviola

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Cambridge Univ Press2024

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

Yimon Aye

Borrowing some quotes from Harper Lee's novel "To Kill A Mockingbird" to help frame our manuscript, we discuss methods to profile local proteomes. We initially focus on chemical biology regimens that function in live organisms and use reactive biotin speci ...

Wiley-V C H Verlag Gmbh2024

Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Nature Portfolio2024

Network Alignment With Holistic Embeddings

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Thanh Trung Huynh

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

IEEE COMPUTER SOC2023

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Engineering novel protein interactions with therapeutic potential using deep learning-guided surface design

Anthony Marchand

Proteins are foundational biomolecules of life playing a crucial role in a myriad of biological processes. Their function often requires interplay with other biomolecules, including proteins themselves. Protein-protein interactions (PPIs) are essential for ...

EPFL2024

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Panagiotis Sioulas

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

EPFL2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Olga Fink

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

PERGAMON-ELSEVIER SCIENCE LTD2023