Efficient binarized algorithm detects seizures from iEEG with one-shot learning.
problem Detecting seizures from iEEG data efficiently and accurately.
method Combines local binary patterns with hyperdimensional computing for end-to-end binary operations.
result Algorithm learns from one or two seizures and generalizes on 27 further seizures.
Modeling and estimating dynamic graphs from binary pattern sequences.
problem Extracting dominant correlation structures from time-dependent binary patterns.
method State-space model of an Ising-type network composed of multiple undirected graphs, sequential Bayes algorithm.
result The method outperforms traditional methods in uncovering overlapping graphs and estimating dynamics of weights.
Archetypal analysis helps understand binary data sets.
problem Explaining binary questionnaire data.
method Using archetypal analysis for binary observations.
result The approach contributes to understanding binary data sets.
Paper studies binary random projections with controllable sparsity patterns for computational and accuracy advantages.
problem Improving computational efficiency and accuracy in random projections.
method Proposes two sparse binary projection models with controllable sparsity patterns.
result Significant computational advantages and improved accuracies in empirical evaluations.
Binary feedback outperforms ordinal comparisons in ranking recovery.
problem Challenges the conventional wisdom that ordinal comparisons offer richer information.
method Proposes a parametric framework for modeling ordinal paired comparisons, binarizing ordinal data, and proving faster convergence rates for binary comparisons.
result Binarizing ordinal data significantly improves ranking recovery accuracy and exhibits a substantial performance gap.
We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose i) a novel algorithm to efficiently separate, i.e., detect and localiz…
The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only i…
CEDA analyzes large categorical datasets using tree geometry and binary codes.
problem Analyzing large categorical datasets with extreme-K samples. method CEDA uses tree geometry and binary codes to analyze categorical data.
result CEDA discovers patterns and evaluates their reliability in large categorical datasets.
QNNs can't distinguish binary signals from their negations, revealing a new symmetry.
problem Understanding the behavior of QNNs in binary pattern classification.
method Presented and analyzed a new form of invariance (negational symmetry) in QNNs.
result QNNs cannot differentiate a quantum binary signal and its negational counterpart in binary classification tasks.
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern recognition and machine learning algorithms. Special cost functions, known as proper scoring rules form natural objective functions to judge the…
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
Develops a fast BMF approach for binary matrices.
problem Finding patterns in binary matrices for various applications.
method MEBF (Median Expansion for Boolean Factorization) using geometric segmentation and heuristic submatrix identification.
result Superior performance in reconstruction error and computational efficiency compared to existing methods.
A Bayesian Boolean Matrix Factorization for cancer genomics
problem Identifying coordinated feature changes in cancer
method Bayesian Boolean Matrix Factorization
result Captures widespread, near-simultaneous chromosome-number changes
BIND removes background noise from binary matrices, improving detection accuracy and fairness.
problem Real data often violates the i.i.d assumption for binary matrix entries, leading to inaccurate detection.
method BIND optimizes detection by estimating row- and column-wise mixture distributions and eliminating background noise.
result BIND effectively removes background noise and increases detection accuracy and fairness.
Improved PQM for pattern classification on quantum computers.
problem Pattern classification in quantum computing.
method Parametric Probabilistic Quantum Memory (PQM) with quantum circuit.
result Classical evaluation and quantum experiments validate PQM viability.
Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.
problem Detecting structure in noisy or approximate repeats of patterns in sparse binary data.
method Probabilistic binary latent variable model based on Noisy-OR model, inferring sparse activity in latent variables.
result Model successfully extracts and explains latent structure in spiking neural data.
Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.
problem Learning patterns in infinite binary sequences.
method Various learning methods are compared, finding no method consistently outperforms others.
result No learning method consistently outperforms others in predicting binary sequences.
Proposes CMP method for reducing tensor object dimensions in binary classification.
problem Reduction of tensor object dimensions while maintaining class separability.
method Proposes Common Mode Patterns (CMP) method considering class labels.
result CMP method increases inter-class separability compared to MPCA.
New method trains Boltzmann machines without supervision.
problem Training unsupervised learning models.
method Mixed binary quadratic feasibility problem formulation.
result Theory validated on XOR patterns.
Extends quadratic loss for SVM and deep learning to improve pattern correlation.
problem Improving generalization in supervised binary classification and regression tasks.
method Extends quadratic loss, restarts from problem (8) in [3], proposes new algorithms, uses multiple kernel learning.
result Comparable results with standard losses and parameterized quadratic loss.
Proposes multi-neighborhood LBPs for land use classification.
problem Challenges in classifying land use images due to intra class variability and inter class similarities.
method Uses multi-neighborhood LBPs combined with nearest neighbor classifier.
result Achieved an accuracy of 77.76% on UC Merced 21 class land use image dataset.
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…
This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming o…
We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset. The classifier is described by a nonlinear ReLU model and the objective function adopts the exponential loss function. We first characterize the landscape of the loss function and show th…
Deep neural networks' loss surfaces contain every low-dimensional pattern.
problem Finding arbitrary low-dimensional patterns in neural network loss surfaces.
method Empirical and theoretical analysis of loss landscapes of deep neural networks.
result Deep universal approximators exhibit a property where arbitrary smooth patterns exist in their loss surfaces.
Machine learning struggles to predict binary options movements due to randomness.
problem Predicting binary options movements using machine learning.
method Tested multiple machine learning models (RF, LR, GB, kNN) and neural networks (MLP, LSTM) on EUR/USD currency pairs.
result None of the models surpassed the ZeroR baseline accuracy, indicating randomness in binary options.
Privacy-preserving binary classification using locally differential private data.
problem Classifying data while protecting individual privacy.
method Locally differential private mechanism followed by a universally consistent classifier.
result Minimax rates of convergence are slower when using private data.
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary va…
Multimodal clustering is an unsupervised technique for mining interesting patterns in n-adic binary relations or n-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n…
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
problem Training WTA-SNNs for multi-valued events is challenging due to non-differentiability and recurrent behavior.
method Develops a variational online local training rule (VOWEL) for WTA-SNNs using local pre- and post-synaptic information and a common reward signal.
result VOWEL outperforms conventional binary SNNs in real-world neuromorphic datasets with multi-valued events.
EagleEye detects localized density anomalies in multivariate data.
problem Identifying signal events, regime changes, or model mismatch in scientific data.
method EagleEye pinpoints local over- and under-densities by assigning anomaly scores based on binary membership sequences and binomial null models.
result EagleEye can detect genuine local anomalies and estimate background purity.
Proposes a new SVM model for binary classification with theoretical and practical advantages.
problem Binary classification in supervised learning.
method Quadratic surface support vector machine with L1 norm regularization.
result The model can detect true sparsity patterns and is efficient for both synthetic and real data.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
GraLSP improves graph neural networks by incorporating local structural patterns.
problem GNNs struggle with identifying common structural patterns in graphs.
method GraLSP uses random anonymous walks to capture local graph structures and incorporates these into feature aggregation mechanisms.
result GraLSP outperforms other models in various prediction tasks on multiple datasets.
The study revisits Hopfield's associative memory model and calculates its capacity for two specific pattern basins.
problem Determining the capacity of a Hebbian-Hopfield network for storing binary patterns.
method Using fully lifted random duality theory and numerical analysis, the study calculates the capacity for two specific pattern basins.
result Explicit characterizations of the capacity for the AGS and NLT pattern basins, with remarkable fast lifting convergence.
Minimal triangulations of circle bundles linked to circular permutations.
problem Which circle bundles can be triangulated over a given base triangulation?
method Minimal triangulations encoded by local systems of circular permutations of vertices.
result Classical Huntington transitivity axiom for cyclic orders expressed as a binary Chern cocycle.
The paper examines topological features of ReLU networks and their relation to decision boundaries and training loss.
problem Understanding the topological structure of ReLU neural network activation patterns.
method Polytope decomposition of feature space, Fiedler partition of dual graph, homology computation of cellular decomposition.
result The Fiedler partition of the dual graph correlates with decision boundaries in binary classification tasks, and similar patterns in training loss and polyhedral cell-count emerge in regression tasks.
Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of th…
FedSLIM optimizes compact pattern models across distributed databases without sharing raw data.
problem Privacy-preserving federated descriptive analytics for data silos.
method Federated MDL-based framework using SLIM principle.
result FedSLIM variants preserve high-quality compression structure and recover globally informative patterns.
Study binary hypothesis testing with privacy and communication constraints.
problem Binary hypothesis testing under local differential privacy and communication constraints.
method Qualifies results as minimax or instance optimal, develops instance-optimal algorithms.
result Achieves minimum possible sample complexity under both privacy and communication constraints.
Understanding optimal prompts for binary sequence predictors is challenging.
problem Finding good prompts for binary sequence predictors is difficult.
method Viewing prompting as finding the best conditioning sequence on a near-optimal sequence predictor, using empirical and statistical analysis.
result Optimal prompts can be better understood given the pretraining distribution, which is not usually available.
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
Paper shows similarity learning can lead to strong binary classification performance.
problem How similarity learning can lead to good classification performance.
method Product-type formulation of similarity learning is connected to binary classification through an excess risk bound.
result Similarity learning can directly elicit a decision boundary for binary classification.
RBMs model binary interactions with hidden node activation effects.
problem Understanding how RBM hidden node activation affects binary variable distributions.
method Investigated RBM marginal distributions with different hidden node activation functions.
result Found exact expressions for RBM marginals as interacting binary variables.
The dimer model and circle patterns are linked via combinatorial and geometric transformations.
problem Understanding the dimer model and its geometric counterpart.
method Established a correspondence between dimer models and circle patterns, using combinatorial and geometric transformations.
result The Miquel dynamics on circle patterns is governed by the octahedron recurrence.
Quantum walks blend patterns into splines when averaged.
problem Understanding the asymptotic patterns of quantum random walks.
method Averaging over quantum coins using the Haar measure.
result Patterns blend into splines, showing a unified behavior.
New method extracts hidden phases in binary mixtures using tubular tilings.
problem Hidden phases in binary mixtures are difficult to observe.
method Introduce tubular tilings for discretizing binary mixtures on smooth manifolds.
result Recover topological information about hidden phases from observable phases and interfaces.
Neural networks can detect weak patterns hidden in noise.
problem Detecting weak patterns in noisy data.
method Developed a three-layer Sejnowski machine with redundant representation, showing patterns can be stored and retrieved efficiently.
result Neural networks can retrieve information with intensity O(1) even in the presence of noise O(\sqrt{N}) in the large N limit.