Local-HDP learns independent topics for each 3D object category in real-time.
problem Learning independent topics for each 3D object category in real-time.
method Local-Hierarchical Dirichlet Process (Local-HDP) with online variational inference.
result Local-HDP outperforms other approaches in accuracy, scalability, and memory efficiency.
Logistic LDA applies LDA to non-categorical data.
problem Applying LDA to non-categorical data is challenging.
method Logistic LDA, a discriminative variant of LDA.
result Logistic LDA can learn from unlabeled data.
ETM discovers interpretable topics in large vocabularies.
problem Existing topic models fail with large, heavy-tailed vocabularies.
method Generative model combining topic models and word embeddings with variational inference.
result ETM discovers interpretable topics even with large vocabularies.
The study analyzes stock price reactions to diverse financial disclosures.
problem Understanding stock price reactions to varied financial disclosures.
method Topic modeling using Latent Dirichlet Allocation (LDA) for categorizing filings.
result Significant abnormal returns in response to specific types of disclosures.
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response var…
A new topic model uses word embeddings on a sphere for better topic coherence.
problem Traditional topic models ignore semantic word correlations.
method Proposes von Mises-Fisher distribution for word density on a unit sphere, using Hierarchical Dirichlet Process and Stochastic Variational Inference.
result The model outperforms existing methods in topic coherence.
D-ETM models document topics over time using embeddings and variational inference.
problem Capturing evolving topic patterns in sequential documents.
method Combines D-LDA and word embeddings, using random walk priors and variational inference.
result D-ETM outperforms D-LDA on document completion tasks, learning more diverse and coherent topics.
Researchers use LRP to explain CNN predictions in NLP tasks.
problem Explaining predictions of complex non-linear classifiers in NLP.
method Layer-wise relevance propagation (LRP) applied to a CNN for topic categorization.
result LRP highlights relevant words for CNN predictions, validating its suitability for NLP.
PM-LDA models allow partial topic memberships for image patches.
problem Crisp segmentation limits topic models to one topic per image patch.
method Developed PM-LDA for partial topic memberships and an estimation algorithm.
result PM-LDA can produce both crisp and soft segmentations.
Improved bibliographic model for author, topic, and document clustering.
problem Modeling research publications using authors, categorical labels, and citation networks.
method Citation Network Topic Model (CNTM) combining Poisson mixed-topic and author-topic models with a novel inference algorithm.
result Improved performance in model fitting and document clustering compared to baselines.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
problem Efficient but contextually limited outputs from existing SLR frameworks.
method Human-in-the-loop process, structured knowledge sources, retrieval-augmented generation.
result Empirical demonstration of AI-driven literature synthesis in option pricing.
Report on enhancing trust in ML models with visualizations.
problem Understanding and trusting the results of black box ML models.
method Categorization of visualization techniques, statistical overview, topic analyses, and interactive web-based survey.
result Expanded categorization of trust against different facets of interactive ML.
Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure …
This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedd…
New imputation method for time series with categorical variables.
problem Missing values in multivariate time series data.
method Expectation Maximization over dynamic Bayesian networks.
result Outperforms state-of-the-art methods in synthetic and real data.
ctsfeatures R package analyzes categorical time series data.
problem Lack of tools for analyzing categorical time series data.
method Provides functions for extracting statistical features and constructing graphs.
result Package enables traditional machine learning tasks on categorical time series.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Paper proposes a new estimator for generic discrete distributions.
problem Estimating gradients for stochastic nodes in deep generative models.
method Generalized Gumbel-Softmax estimator using truncation, Gumbel-Softmax trick, and linear transformation.
result Efficacy and practical value demonstrated in synthetic examples and topic models.
Interprets neural network classifiers for categorical inputs.
problem Neural networks' interpretability in human-sensitive applications.
method Mapping to physical energy model, expansion of neural network layers.
result Each layer's contribution to classification can be analyzed.
Survey on reproducibility and distortion issues in text clustering and topic modeling.
problem Reproducibility and misleading cluster geometry in unsupervised learning for text categorization.
method Systematic literature review of text clustering and topic modeling from 2011-2022.
result Outliers and initialization issues are significant factors in text clustering and topic modeling.
Dynamic topic model improves mental health note analysis for children.
problem Lack of longitudinal topic models for psychiatric clinical notes.
method Developed a dynamic topic model with consistent topics and individualized temporal dependencies.
result Achieved a 38% increase in topic coherence.
Paper proposes a semi-supervised learning approach for automated topic detection in support and feedback data.
problem Manual analysis of large textual feedback and support data is time-consuming and labor-intensive.
method Combines BERT-based multiclassification with a novel PSHTI model for automated topic and sub-topic inference.
result Automated system for better understanding user voice and engagement patterns in enterprise data.
Discusses new probabilistic morphisms and geometric methods in machine and statistical learning.
problem Addressing challenges in statistical, machine, and manifold learning.
method Introduces category of probabilistic morphisms and geometric methods.
result New insights and applications in various learning fields.
Study categorizes knots and links as rigid or shaky based on Reidemeister moves.
problem Classifying knots and links as rigid or shaky based on adaptability to Reidemeister moves.
method Categorization of hard diagrams as rigid or shaky, investigation of rigid and shaky hard diagrams for specific knots and links.
result Every link has a rigid hard diagram, and there is an upper limit for the number of crossings in such diagrams.
Algorithm learns diverse rankings for search engines.
problem Designing algorithms for search engines to rank diverse items.
method LDR (Learning Diverse Rankings) algorithm, efficient learning based on users' feedback.
result Algorithm achieves optimal ranking performance with O((N−L)log(T)) regret. AI attacks threaten insurance systems, requiring new defenses.
problem Adversarial attacks on AI in insurance.
method Categorize and discuss various types of attacks and defense methods.
result Need for improved AI systems to resist attacks.
Following Roe and others (see, e.g., [MR1451755]), we (re)develop coarse geometry from the foundations, taking a categorical point of view. In this paper, we concentrate on the discrete case in which topology plays no role. Our theory is particularly suited to the development of the_Roe (C*-)algebras_ C*(X) and their K…
Machine learning identifies types of alterations in historical manuscripts.
problem Understanding and categorizing alterations in historical manuscripts.
method Alteration Latent Dirichlet Allocation (alterLDA) model.
result High performance in recognizing alterations on labelled data, and interesting insights on unlabelled data.
Machine learning explains text document categorization decisions.
problem Understanding how text documents are categorized by machine learning models.
method Layer-wise relevance propagation (LRP) to trace predictions back to individual words.
result Word-based ML models can be made more comprehensible through LRP.
Study uses machine learning to analyze Twitter sentiments about COVID-19.
problem Examining public concerns and sentiments about COVID-19 from Twitter.
method Machine learning (Latent Dirichlet Allocation) to identify topics and sentiments.
result Identified 13 topics and categorized into five themes, revealing dominant fears and mixed feelings.
Unified taxonomy categorizes DL-based MTSAD methods.
problem Lack of systematization in MTSAD research.
method Two-fold approach: derived from methodological studies and reviewed papers.
result Convergence toward Transformer-based and reconstruction/prediction models.
Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks…
Survey on federated learning systems for privacy protection.
problem Privacy-preserving collaborative training of machine learning models.
method Comprehensive review and categorization of federated learning systems.
result Systematic summary of existing federated learning systems and design factors.
New models automate support group formation in online health communities.
problem Challenges in traditional support group formation methods for scalability, static categorization, and insufficient personalization.
method Two novel machine learning models: gDMR and gSTM, integrating user content, demographics, and network data.
result Models outperform baselines in predictive accuracy, semantic coherence, and internal group consistency.
Survey on modeling event sequences through temporal processes.
problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.
StructureBoost improves gradient boosting for complex categorical variables efficiently.
problem Efficiently handling complex categorical variables with known structure.
method Two methods to overcome computational obstacles in SCDT enumeration for structured categorical variables.
result StructureBoost outperforms existing packages on complex categorical problems.
Categorical bundles provide a natural framework for gauge theories involving multiple gauge groups. Unlike the case of traditional bundles there are distinct notions of triviality, and hence also of local triviality, for categorical bundles. We study categorical principal bundles that are product bundles in the categor…
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
A new method for backpropagating through categorical distributions.
problem Difficulty in backpropagating through categorical latent variables in neural networks.
method Introducing Gumbel-Softmax distribution for differentiable sampling.
result Gumbel-Softmax estimator outperforms existing methods on tasks with categorical latent variables.
microTC is a minimalistic text classifier for various tasks.
problem Text categorization tasks across different domains and languages.
method Minimalistic approach with text transformations, representations, and supervised learning.
result microTC outperformed state-of-the-art methods in 20 out of 30 datasets.
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cas…
UNTIE learns representations of coupled categorical data.
problem Challenges in learning from unlabeled categorical data with complex couplings.
method UNTIE approach for unsupervised representation learning of heterogeneous couplings.
result UNTIE significantly improves categorical data representations on 25 diverse datasets.
Paper introduces Categorical Normalizing Flows for better handling of categorical data.
problem Limited application of normalizing flows on categorical data due to lack of intrinsic order.
method Categorical Normalizing Flows use continuous transformations to model latent relations in categorical data, optimizing both continuous representation and model likelihood.
result GraphCNF, a permutation-invariant generative model, outperforms state-of-the-art on molecule generation.
Authors conjecture categorically diagonalizable complex for full twists.
problem Categorically diagonalizing the complex of Soergel bimodules for full twists.
method Utilizes categorical diagonalization theory.
result Proves conjecture in type A, categorifies Young idempotents.
This paper proposes a method to reduce complexity in GLMs with categorical predictors.
problem Wasteful, hard-to-interpret, and prone to overfitting of traditional one-hot encoding for high-cardinality categorical predictors.
method Clustering categories of categorical predictors through a numerical method that preserves or improves accuracy while reducing the number of coefficients.
result Clustering categories of categorical predictors reduces complexity substantially without harming accuracy.
Gaussian-Dirichlet posterior dominance proven for sequential categorical data.
problem Sequential learning from categorical observations bounded in [0,1]
method Establishing an ordering between Dirichlet and Gaussian posteriors under N(0,1) noise
result Posterior mean of categorical distribution stochastically dominates Gaussian distribution
A new method optimises problems with both continuous and categorical inputs.
problem Optimising black-box problems with mixed continuous and categorical inputs.
method Continuous and Categorical Bayesian Optimisation (CoCaBO) combining multi-armed bandits and Bayesian optimisation.
result CoCaBO outperforms existing methods on synthetic and real-world tasks.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.