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

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

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 ...

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 ...

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 ...

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

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 ...

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 ...

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

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 ...

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. ...

Porous membranes are thin solid structures that allow the flow to pass through their tiny openings, called pores. Flow inertia may play a significant role in several filtration flows of natural and engineering interest. Here, we develop a predictive macros ...

We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., Ignore previous instructions and...), we show that it is possible to conceptualize the creation of execution triggers as ...

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus, existing methods often rely on intermedia ...

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 ...

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

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

BRAVE: Broadening the Visual Encoding of Vision-Language Models

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Image-Guided Topic Modeling for Interpretable Privacy Classification

Uncertainty in Modern Deep Learning

Structural equation models for multivariate extremes

Machine learning-aided generative molecular design

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

AMCG: a graph dual atomic-molecular conditional molecular generator

Floorplan Isovist Dataset

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

Quasi-linear homogenization for large-inertia laminar transport across permeable membranes

Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance