Automated model tracks mouse behavior in home cages.
problem Confounding analysis in specialized arenas.
method Group Behaviour Model (GBM) using permutation matrix.
result Model captures individual and group behavior.
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48 results for “mouse behavioral data”
Machine learning detects survey validity from user behavior.
problem Detecting valid responses in web surveys.
method Uses mouse activity and machine learning models (LSTM, HMM).
result Predicts survey validity without analyzing specific answers.
Neural nets predict user attention from mouse movements.
problem Predicting user attention from mouse cursor movements.
method Investigated different mouse movement representations and trained neural networks.
result Neural networks outperform handcrafted features for predicting user attention.
We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various…
New ensemble models classify mouse movement trajectories to assess survey question difficulty.
problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.
A new method fuses labels from multiple, differently-annotated schemas.
problem Learning from labels that use different schemas.
method Inter-Schema AdapteR (ISAR) to translate labels.
result Significant gains in log-likelihood and F1-score.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
problem Identifying precise mouse brain microscopy images from the Allen atlas.
method Siamese Networks with contrastive learning to find corresponding atlas plates for partial images.
result Siamese CNNs achieved 25% TOP-1 and 100% TOP-5 accuracy in identifying brain slices from the Allen atlas.
New attacks bypass authentication models using mouse dynamics data.
problem Adversarial attacks on behavioural mouse authentication.
method Generative approaches to create adversarial mouse trajectories.
result Imitation-based attacks often outperform surrogate-based attacks.
Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activitycs.LG
Deep learning predicts stress levels from mouse hippocampus activity.
problem Stress level in mice under different environments.
method Deep learning combined with neuron decoding.
result Deep learning model accurately predicts stress levels.
Feature selection predicts immune state changes in RA mouse model.
problem Predicting the immune state change after RA immunotherapy.
method Feature selection algorithms applied to mouse CIA model data.
result Selected features predict both T cell markers and treatment efficacy.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other …
Composing graphical models with neural networks for structured representations and fast inferencestat.ML
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencode…
Method identifies key features for clustering in high-dimensional data.
problem Understanding hidden patterns in high-dimensional data.
method Unsupervised feature selection based on discriminative power.
result 27 key transcription factors identified, 18 known to define cell states.
Cellina uses supervised disentanglement to predict cell behavior in tissues.
problem Querying counterfactuals on tissue graphs
method Cellina framework using supervised disentanglement
result Outperforms spatially-informed and non-spatial competitors
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomicsstat.ML
Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissu…
Dreamer 4 learns Minecraft tasks from videos alone.
problem Accurately predicting object interactions in complex environments.
method Reinforcement learning inside a fast, accurate world model.
result Dreamer 4 outperforms previous models in Minecraft, learning from only offline data.
DCNs mimic neuronal networks for improved neural classification.
problem Lack of topological similarity between DNNs and biological neural networks.
method Developed DCNs with topologies inspired by real-world neuronal networks.
result High classification accuracy achieved by DCNs.
Although we have tons of machine learning tools to analyze data, most of them require users have some programming backgrounds. Here we introduce a SaaS application which allows users analyze their data without any coding and even without any knowledge of machine learning. Users can upload, train, predict and download t…
COOS-7 dataset benchmarks image classifier generalization.
problem Measuring generalization of image classifiers to out-of-sample datasets.
method Created COOS-7 dataset with varying covariate shifts; benchmarked multiple models.
result All classifiers failed to generalize to datasets with greater covariate shifts.
Deep learning models outperform classical methods in forecasting neural activity.
problem Improving forecasting of neural activity using deep learning models.
method Systematic evaluation of eight probabilistic deep learning models against classical statistical models and baseline methods.
result Several deep learning models consistently outperform classical approaches in forecasting neural activity.
Decodes neural activity to detect context effects in natural settings.
problem Detecting context-dependent changes in neural encoding.
method Decoding-based approach controlling for confounding factors.
result Demonstrates context-dependent changes in neural encoding.
Identification of patterns from discrete data time-series for statistical inference, threat detection, social opinion dynamics, brain activity prediction has received recent momentum. In addition to the huge data size, the associated challenges are, for example, (i) missing data to construct a closed time-varying compl…
Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the…
New model uncovers non-Euclidean neural representations.
problem Discovering latent neural states in complex, non-Euclidean spaces.
method Manifold GPLVM for identifying latent variables and neural contributions.
result mGPLVM correctly recovers non-Euclidean latent structures in neural data.
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic genera…
Malware authors have always been at an advantage of being able to adversarially test and augment their malicious code, before deploying the payload, using anti-malware products at their disposal. The anti-malware developers and threat experts, on the other hand, do not have such a privilege of tuning anti-malware produ…
Sparse clustering, which aims to find a proper partition of an extremely high-dimensional data set with redundant noise features, has been attracted more and more interests in recent years. The existing studies commonly solve the problem in a framework of maximizing the weighted feature contributions subject to a $\ell…
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this paper presents and compares alternative approaches - timestamps and difference images - to incorporate temporal information for the classificat…
Paper analyzes adversarial attacks and defenses using game theory.
problem Unclear conditions for optimal attacks and defenses in adversarial learning.
method Game-theoretic framework with locally linear decision boundary model.
result Fast Gradient Method attack and Randomized Smoothing defense form a Nash Equilibrium.
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.
Sparse Convex Biclusteringstat.ML
Sparse Convex Biclustering improves accuracy and robustness in high-dimensional datasets.
problem Challenges in clustering rows and columns of large-scale datasets due to noise and computational complexity.
method Sparse Convex Biclustering (SpaCoBi) using convex optimization and stability-based tuning.
result Significantly outperforms state-of-the-art methods in accuracy for high-dimensional datasets.
Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neuro…
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the…
Developed a multiway classification method for sparse data.
problem Classification of multiway arrays with sparsity.
method Extended Distance Weighted Discrimination (DWD) to multiway context, accounting for sparsity.
result Improves classification accuracy in multiway structured data.
MoE-Sim-VAE clusters and generates data from similarity-based representations.
problem Clustering high-dimensional data like biological measurements.
method Variational Autoencoder with Mixture-of-Experts architecture.
result MoE-Sim-VAE outperforms other methods in clustering and generating realistic data.
Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hid…
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject. While using the element-wise sampl…
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are regarded as new explanatory variables in the model. Note that PCA is based only on the explanatory variables, so the principal components a…
Develops probabilistic models for gene regulatory network inference.
problem Challenges in reconstructing gene regulatory networks from genome-wide data.
method Two complementary frameworks: PMF-GRN and GLM-Prior.
result Probabilistic inference refines regulatory estimates with quantified uncertainty.
Active learning method for neural population dynamics using optogenetics.
problem Efficiently selecting neurons to stimulate for identifying neural population dynamics.
method Developed active learning procedure for low-rank regression to determine informative photostimulation patterns.
result Demonstrated a two-fold reduction in data required for predictive power using low-rank linear dynamical systems model.
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networksq-bio.QM
Graph auto-encoder predicts unobserved node features from biological networks and omics data.
problem Integrating biological networks and continuous node features for better prediction.
method Graph neural networks and feature auto-encoders trained on feature reconstruction.
result Graph feature auto-encoder outperforms auto-encoders trained on graph reconstruction for predicting unobserved node features.
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihoodstat.ME
Bayesian method learns graph structures from Gaussian data efficiently.
problem Scalability issue in Bayesian Gaussian graphical model inference.
method Marginal pseudo-likelihood, birth-death and reversible jump MCMC algorithms.
result Efficient graph structure learning for large graphs with over 1,000 nodes.
Method detects batch heterogeneity in genomic data.
problem Batch effects confound genomic diagnostics.
method Bayesian model evidence clustering.
result Detects batch effects without known labels.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signaturescs.LG
Detects adversarial examples using SHAP values from neural networks.
problem Vulnerability of deep neural networks to adversarial attacks.
method Uses SHAP values from internal layers to distinguish normal from adversarial inputs.
result Demonstrates high detection accuracy and strong generalization to various attack methods.
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from H…