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Found 31 papers, 20 papers with code
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e. g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure.
The Essential Role of Causality in Foundation World Models for Embodied AI
The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
These results provide compelling evidence that our method has the potential to serve as a stepping stone for the development of causal foundation models.
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.
High Fidelity Image Counterfactuals with Probabilistic Causal Models
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models.
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error.
Understanding Causality with Large Language Models: Feasibility and Opportunities
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
Measuring axiomatic soundness of counterfactual image models
We present a general framework for evaluating image counterfactuals.
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions).
Deep Structural Causal Shape Models
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association.
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data.
Structured Uncertainty in the Observation Space of Variational Autoencoders
In contrast, improving the model for the observational distribution is rarely considered and typically defaults to a pixel-wise independent categorical or normal distribution.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Simultaneous Missing Value Imputation and Structure Learning with Groups
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.
Deep Structural Causal Models for Tractable Counterfactual Inference
We formulate a general framework for building structural causal models (SCMs) with deep learning components.
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution for a given input.
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework.
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.
Needles in Haystacks: On Classifying Tiny Objects in Large Images
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.
Is Texture Predictive for Age and Sex in Brain MRI?
Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdependencies.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
We demonstrate the use of the method in a task of disentangling global structure from local features in images.
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
Rasa: Open Source Language Understanding and Dialogue Management
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software.
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images.
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation.
Implicit Weight Uncertainty in Neural Networks
Modern neural networks tend to be overconfident on unseen, noisy or incorrectly labelled data and do not produce meaningful uncertainty measures.
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key.
Efficient variational Bayesian neural network ensembles for outlier detection
In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting.
