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Found 32 papers, 17 papers with code
Naive Bayes Classifiers and One-hot Encoding of Categorical Variables
This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"{\i}ve Bayes classifier.
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages
Behavioural experiments often happen in specialised arenas, but this may confound the analysis.
Structured Generative Models for Scene Understanding
This approach also requires scene models which account for the co-occurrences and inter-relationships of objects in a scene.
Multi-Task Dynamical Systems
Time series datasets are often composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations.
Inference and Learning for Generative Capsule Models
In this paper we specify a generative model for such data, and derive a variational algorithm for inferring the transformation of each model object in a scene, and the assignments of observed parts to the objects.
On Suspicious Coincidences and Pointwise Mutual Information
We then discuss the mutual information (MI) and pointwise mutual information (PMI), which depend on the ratio $P(A, B)/P(A)P(B)$, as measures of association.
Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences
Given two object images, how can we explain their differences in terms of the underlying object properties?
Persistent Animal Identification Leveraging Non-Visual Markers
Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research.
Identifying the Units of Measurement in Tabular Data
We consider the problem of identifying the units of measurement in a data column that contains both numeric values and unit symbols in each row, e. g., "5. 2 l", "7 pints".
ptype-cat: Inferring the Type and Values of Categorical Variables
Type inference is the task of identifying the type of values in a data column and has been studied extensively in the literature.
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain.
On Memorization in Probabilistic Deep Generative Models
Recent advances in deep generative models have led to impressive results in a variety of application domains.
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.
Inference for Generative Capsule Models
Capsule networks (see e. g. Hinton et al., 2018) aim to encode knowledge and reason about the relationship between an object and its parts.
The Effect of Class Imbalance on Precision-Recall Curves
In this note I study how the precision of a classifier depends on the ratio $r$ of positive to negative cases in the test set, as well as the classifier's true and false positive rates.
VAEs in the Presence of Missing Data
Real world datasets often contain entries with missing elements e. g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests.
An Evaluation of Change Point Detection Algorithms
Next, we present a benchmark study where 14 algorithms are evaluated on each of the time series in the data set.
ptype: Probabilistic Type Inference
Type inference refers to the task of inferring the data type of a given column of data.
Customizing Sequence Generation with Multi-Task Dynamical Systems
Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application.
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data
We show experimentally that not only RVAE performs better than several state-of-the-art methods in cell outlier detection and repair for tabular data, but also that is robust against the initial hyper-parameter selection.
The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas
While label fusion from multiple noisy annotations is a well understood concept in data wrangling (tackled for example by the Dawid-Skene (DS) model), we consider the extended problem of carrying out learning when the labels themselves are not consistently annotated with the same schema.
Multi-Task Time Series Analysis applied to Drug Response Modelling
Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients.
Learning Direct Optimization for Scene Understanding
We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x.
Inverting Supervised Representations with Autoregressive Neural Density Models
In addition, we can use these inversion models to estimate the mutual information between a model's inputs and its intermediate representations, thus quantifying the amount of information preserved by the network at different stages.
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case
We show how to calculate exactly the latent posterior distribution for the factor analysis (FA) model in the presence of missing data, and note that this solution implies that a different encoder network is required for each pattern of missingness.
A Framework for the Quantitative Evaluation of Disentangled Representations
Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors behind data.
Model Criticism in Latent Space
Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e. g. Gelman, Carlin, Stern, and Rubin [2004, p. 165].
Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers
Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly, slowing response times and desensitising nurses to alarms (Chambrin, 2001), causing true alarms to be missed (Hug et al., 2011).
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring
We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs).
Tree-Cut for Probabilistic Image Segmentation
This paper presents a new probabilistic generative model for image segmentation, i. e. the task of partitioning an image into homogeneous regions.
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring
We present a Discriminative Switching Linear Dynamical System (DSLDS) applied to patient monitoring in Intensive Care Units (ICUs).
Renewal Strings for Cleaning Astronomical Databases
Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious records coming from artefactual effects of the telescope, satellites and junk objects in orbit around earth and physical defects on the photographic plate or CCD.
