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Found 25 papers, 16 papers with code
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
The estimation of causal effects with observational data continues to be a very active research area.
Disentangling representations of retinal images with generative models
Retinal fundus images play a crucial role in the early detection of eye diseases and, using deep learning approaches, recent studies have even demonstrated their potential for detecting cardiovascular risk factors and neurological disorders.
Self-supervised Visualisation of Medical Image Datasets
Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widely used prior to supervised fine-tuning.
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging.
A computational approach to visual ecology with deep reinforcement learning
To study how environments shape and constrain visual processing, we developed a deep reinforcement learning framework in which an agent moves through a 3-d environment that it perceives through a vision model, where its only goal is to survive.
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models
Counterfactual reasoning is often used in clinical settings to explain decisions or weigh alternatives.
Persistent homology for high-dimensional data based on spectral methods
As a remedy, we find that spectral distances on the $k$-nearest-neighbor graph of the data, such as diffusion distance and effective resistance, allow persistent homology to detect the correct topology even in the presence of high-dimensional noise.
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
We formulate subgroup shift detection in the framework of statistical hypothesis testing and show that recent state-of-the-art statistical tests can be effectively applied to subgroup shift detection on medical imaging data.
AI for Science: An Emerging Agenda
This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.
Efficient identification of informative features in simulation-based inference
To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to estimate a surrogate likelihood or posterior.
Unsupervised visualization of image datasets using contrastive learning
This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization.
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
Here, we show how a family of such two-stage models can be combined into a single, hierarchical model that we call a hierarchical mixture of Gaussians (HMoG).
Sparse Visual Counterfactual Explanations in Image Space
Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decision of the classifier would change.
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience
Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron.
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms.
Attraction-Repulsion Spectrum in Neighbor Embeddings
Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using $k$NN graphs.
Sparse bottleneck neural networks for exploratory non-linear visualization of Patch-seq data
Patch-seq, a recently developed experimental technique, allows neuroscientists to obtain transcriptomic and electrophysiological information from the same neurons.
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse
The inherent noise of neural systems makes it difficult to construct models which accurately capture experimental measurements of their activity.
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique.
Signatures of criticality arise in simple neural population models with correlations
Support for this notion has come from a series of studies which identified statistical signatures of criticality in the ensemble activity of retinal ganglion cells.
Neurons and Cognition
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces.
A joint maximum-entropy model for binary neural population patterns and continuous signals
Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains.
Neurometric function analysis of population codes
In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding.
