AMCG: a graph dual atomic-molecular conditional molecular generator

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The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...

In this thesis, we address a fundamental challenge in modern deep learning: uncertainty estimation. While deep neural networks have achieved remarkable success across diverse and impactful applications-from robotics to large language models and advanced in ...

Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the ...

Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propos ...

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein coupled receptors (aGPCRs). While recent structural studies support aGPCR activation through shedding of t ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we ...

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can ...

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

This thesis is dedicated to developing innovative methodologies that improve elemental quantification in scanning transmission electron microscopy (STEM) using energy-dispersive X-ray spectroscopy (EDXS). The primary motivation stems from a geochemistry pr ...

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (ed ...

AMCG: a graph dual atomic-molecular conditional molecular generator

Chat with Graph Search

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Uncertainty in Modern Deep Learning

BRAVE: Broadening the Visual Encoding of Vision-Language Models

Image-Guided Topic Modeling for Interpretable Privacy Classification

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Molecular characterization of an adhesion GPCR signal transduction using Graph-neural networks

Machine learning-aided generative molecular design

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

Floorplan Isovist Dataset

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Image sensitive spectral response of semiconductor random network lasers

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

New tools for phase segmentation and denoising of analytical STEM data for enhanced chemical sensitivity, applied to Earth mantle research

Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model