New method counts boundary pieces in ReLU classifiers for better complexity measure.
problem Current classification complexity measures are misleading and ineffective.
method Developed a novel method using tropical geometry to count exact boundary pieces.
result Boundary piece count is negatively correlated with robustness.
Classify & Count can estimate prevalence without adjustments if optimised for quantification.
problem Estimating prevalence without adjustments using a classifier optimised for quantification.
method Classify & Count approach, optimised for quantification, local Bayes optimality.
result Optimised Classify & Count can estimate prevalence without adjustments in the binormal model.
Sketches linear classifiers using Weight-Median Sketch for efficient data stream analysis.
problem Efficiently learning and analyzing data streams with limited memory.
method Introduces Weight-Median Sketch for compressed linear classifier learning over data streams.
result Memory-limited execution of various analyses over streams, including feature selection and mutual information estimation.
Fisher consistency improves class probability estimation under dataset shift.
problem Lack of Fisher consistency can lead to unreliable class probability estimates.
method Introduced Fisher consistency as a desirable property for class prior probability estimators.
result CDE-Iterate is not Fisher consistent and cannot be trusted for reliable estimates.
Proposes SDWEC for improved classification accuracy and reduced classifier count.
problem Improves classification accuracy and minimizes the number of classifiers.
method Formed an ensemble of pre-trained classifiers with assigned weights, modeled as a non-convex cost function with terms for data fidelity, sparsity, and non-negativity constraints. Employed convex relaxation techniques and approximations for efficient solution.
result SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time.
A new neural network improves sentiment quantification accuracy.
problem Quantifying sentiment prevalence from unlabelled text.
method Recurrent neural network (QuaNet) that learns quantification embeddings from classification predictions.
result QuaNet outperforms state-of-the-art sentiment quantification methods.
We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns. The three distributions we consider are derived from the gamma-Poisson, gamma-negative binomial, and beta-negative binomial processes. Because the models lead to closed-form Gibbs sampling …
The study counts curves on a once-punctured torus with self-intersections.
problem Counting closed curves with self-intersections on a once-punctured torus.
method Combinatorial classification of curves with given word-length and self-intersections.
result Determination of curve counts with zero, one, and arbitrary self-intersections.
We describe a way of representing finite biquandles with n elements as 2n x 2n block matrices. Any finite biquandle defines an invariant of virtual knots through counting homomorphisms. The counting invariants of non-quandle biquandles can reveal information not present in the knot quandle, such as the non-triviality o…
We study rack polynomials and the link invariants they define. We show that constant action racks are classified by their generalized rack polynomials and show that nsata-quandles are not classified by their generalized quandle polynomials. We use subrack polynomials to define enhanced rack counting invariants, gen…
A new method aggregates generative classifiers to resist adversarial attacks.
problem Adversarial attacks on deep neural networks.
method Rank-aggregating ensemble of generative classifiers trained on intermediate layer responses.
result The ensemble of generative classifiers shows robustness to adversarial attacks.
Algorithm counts Killing vectors in 3D spacetime.
problem Counting Killing vectors in 3D spacetime.
method Algorithm based on Ricci tensor and differential invariants.
result Complete classification of 4 Killing vector spacetimes.
Deep learning tool classifies urban delivery vehicles.
problem Counting and categorizing delivery vehicles in cities.
method Developed annotated database and retrained CNNs.
result Accurate classification of 90%+ for 3 vehicle classes.
Regular integer lattices are characterized by k unit vectors that build up their generator matrices. These have rank k for D-lattices, and are rank-deficient for A-lattices, for E_6 and E_7. We count lattice points inside hypercubes centered at the origin for all three types, as if classified by maximum infinity norm i…
MTCNet uses MTL to estimate crowd density and count.
problem Crowd count estimation challenges due to scale variations and perspective.
method MTL deep neural network architecture with two tasks: density estimation and count classification.
result Achieves lower MAE than state-of-the-art methods on multiple datasets.
Deep learning model detects and classifies marine microfossils.
problem Manual identification of microfossils is time-consuming and error-prone.
method Transfer learning from ImageNet dataset to classify foraminifera.
result Proposed model achieves high accuracy on foraminifera classification.
Improves classification of microbiome data using mixture distributions.
problem Challenges in classifying sparse and heterogeneous microbiome count data.
method Distance-based classification using mixture distributions.
result The method outperforms existing distance-based classifiers and machine learning approaches.
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about t…
Formulae count square-tiled surfaces in genus two.
problem Counting square-tiled surfaces in genus two.
method Parametrized classification into four diagrams, provided formulae for enumeration.
result Formulae for enumeration of square-tiled surfaces in four diagrams, completing the count for genus two.
Continuous Sweep improves binary quantifier performance.
problem Estimating class prevalence in datasets.
method Parametric binary quantifier inspired by Median Sweep, using parametric class distributions and mean of Adjusted Count estimates.
result Continuous Sweep outperforms other quantifiers in simulations and empirical data analysis.
Classifies invariant measures on specific character varieties.
problem Classifying invariant probability measures on character varieties.
method Measure disintegration along transverse Lagrangian tori fibrations.
result Ergodic measures are either counting measures on finite orbits or Liouville measures.
A CNN-based method detects and counts corn kernels from images.
problem Manual counting of corn kernels is labor-intensive and prone to error.
method Sliding window approach with CNN for detection and NMS for overlapping removal.
result The method successfully detects and counts kernels with low error.
A taxonomy classifies memory networks based on their memory organization.
problem Classifying and understanding the expressive power of different memory networks.
method Developed a taxonomy including RNN, LSTM, neural stack, and neural RAM, analyzing their differences and commonality.
result Showed the relative expressive power of memory networks and how they relate to specific tasks.
Deep learning model classifies Chilean banknotes.
problem Automatic bill classification for applications like counting.
method Introduced data augmentation techniques for limited samples.
result Successfully classified Chilean banknotes with positive results.
Graph classification improved with motif counts and graphon theory.
problem Classifying large graphs with high accuracy.
method Using motif homomorphisms and graphon theory to provide bounds and a classifier.
result Explicit quantitative bounds for graph classification under noise.
HET-XL improves heteroscedastic classifiers for large-scale image classification.
problem Scaling heteroscedastic classifiers to handle large numbers of classes and tuning the temperature hyperparameter.
method HET-XL, a heteroscedastic classifier with independent parameter count from the number of classes, learns the temperature hyperparameter directly from training data.
result HET-XL requires 14X fewer additional parameters and performs better than baseline heteroscedastic classifiers on large image classification datasets.
In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this classification, we develop a Bayesian statistical model that makes use of the fact that …
We discuss the invariant classification of vacuum Kundt waves using the Cartan-Karlhede algorithm, and the upper bound on the number of iterations of the Karlhede algorithm to classify the vacuum Kundt waves. By choosing a particular coordinate system we partially construct the canonical coframe used in the classificat…
Counting tripods on a flat torus using lattice point counting.
problem Counting finite BPS webs in flat torus geometry.
method Lattice point counting techniques in C2. result Asymptotic counting result for tripods on the torus.
Many applications in data analysis begin with a set of points in a Euclidean space that is partitioned into clusters. Common tasks then are to devise a classifier deciding which of the clusters a new point is associated to, finding outliers with respect to the clusters, or identifying the type of clustering used for th…
Flow Matching for count data improves sample quality and efficiency.
problem Mapping between count distributions across batches or time points in high-dimensional count data.
method count-FM, a flow-matching framework based on a continuous-time birth-death process with local unit jumps.
result count-FM achieves better sample quality than representative baselines while using fewer parameters.
Study counts sub-chord diagrams to classify spherical curves.
problem Classifying spherical curves using chord diagrams.
method Counting sub-chord diagrams under specific moves.
result New invariant classifies prime reduced spherical curves.
Counting orbits for Anosov groups with specific functionals.
problem Counting orbits for relatively Anosov groups with linear functionals.
method Equidistribution results and previous counting results for periods.
result Generalization of earlier work on Anosov groups.
Count-ception predicts object counts in images with reduced errors.
problem Counting objects in images is time-consuming and error-prone.
method Fully convolutional redundant counting approach using a Count-ception network.
result 20% relative improvement in accuracy over state-of-the-art methods.
Study uses DHS to classify anemia types using CBC indices.
problem Anemia classification for medical purposes.
method Dynamic Harmony Search (DHS) applied to CBC indices.
result DHS outperforms other models in anemia classification.
New theorem counts curves on orbifolds.
problem Counting curves on surfaces.
method Applied Mirzakhani's theorem to orbifolds.
result Curve counting theorem extends to orbifolds.
MACH reduces memory usage for extreme classification by hashing.
problem Expensive training of deep models with large softmax layers.
method Merged-Average Classifiers via Hashing (MACH) using count-min sketch.
result Significant memory reduction and training speedup.
A new method, Count-MORL, improves offline reinforcement learning by using state-action frequency.
problem Improving offline reinforcement learning performance.
method Integrates count-based conservatism into model-based offline reinforcement learning.
result The learned policy is near-optimal and outperforms existing methods.
Proposes a method to reconcile count time series forecasts.
problem No formal framework for probabilistic reconciliation of count time series.
method Generalizes Bayes' rule for reconciling real-valued and count variables.
result Improves forecast accuracy for count variables compared to Gaussian reconciliation.
Graph neural networks struggle with counting certain substructures in graphs.
problem Detecting and counting specific substructures in graphs.
method Study of graph neural networks' ability to count attributed graph substructures.
result Graph neural networks like MPNNs, 2-WL, and 2-IGNs have limitations in counting certain substructures.
Study geodesic paths on flat surfaces, comparing length and singularity counts.
problem Comparing geometric length and singularity counts on geodesic paths.
method Apply counting limit laws to infinite graphs and then to flat surfaces.
result Statistical comparison of geometric length and singularity counts on geodesic paths.
Deviance-style normalization for sparse, jointly overdispersed count matrices
problem Jointly overdispersed count matrices
method Dirichlet-multinomial deviance residualization
result Preserves exact sparsity, evaluates in constant time, recovers multinomial residual
The paper proposes count echo state networks for forecasting graduate student enrollments.
problem Forecasting graduate student enrollments from historical data.
method Developed hierarchical count echo state networks and compared them to Poisson autoregressions and negative binomial models.
result Hierarchical negative binomial based echo state network is the superior model.
Counts arcs in surfaces, proving convergence of geodesic currents.
problem Counting arcs of the same type in compact surfaces and related geometries.
method Derives convergence of geodesic currents to prove arc counts.
result Proves convergence of geodesic currents, leading to arc counting results.
Counted essential surfaces in a knot's exterior, finding a unique pattern.
problem Counting essential surfaces in a knot's exterior.
method Counted essential surfaces by genus, using Euler totient function. Showed normal surfaces are connected by counting their components. Used Agol, Hass, and Thurston's tools to convert component counting into orbit counting.
result Found a unique pattern in the number of essential surfaces by genus.
The abstract reviews models for analyzing count data.
problem Challenges in analyzing count data with standard methods.
method Review of generalized linear models and multinomial models.
result Fundamental connections between multinomial and count models.
Efficiently counts data streams in machine learning.
problem Counting queries in machine learning applications.
method Abstracting queries and aggregating as a stream for scalability.
result Significantly outperforms ADtrees and hash tables.
Paper proposes a method to estimate uncertainty in counting tasks in medical imaging.
problem Estimating uncertainty in counting tasks for medical imaging.
method Proposes and tests a method for calculating predictive intervals as an output of a multi-task network.
result Demonstrates the effectiveness of the technique on histopathological cell counting and white matter hyperintensity counting.