Geometric method captures rare topics and temporal alignment in co-author networks.
problem Missing rare topics and smooth temporal alignment in topic modeling.
method Integrates multimodal text and co-author network data using Hellinger distances and Ward's linkage.
result Effective identification of rare topics and visualization of topic drift over time.
New models discover new topics over time in topic modeling.
problem Discovering new topics over time in topic modeling.
method Nonparametric Bayesian models and Hungarian matching algorithm.
result Significantly faster than existing methods, discovering new topics in large datasets.
A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal var…
Study uses news trends to predict infectious disease outbreaks.
problem Predicting infectious disease outbreaks using news reports.
method Supervised temporal topic models to transform news articles into trends.
result Temporal topic trends from disease news capture outbreak dynamics.
Improved topic modeling captures temporal relationships in speech.
problem Lack of temporal information in LDA for speech analysis.
method Temporal Markov chain extension to LDA for acoustic unit discovery.
result Improved phone segmentation results compared to base LDA.
New method improves dynamic topic modeling for large-scale data.
problem Lack of temporal information in dynamic topic modeling.
method Nonnegative CP tensor decomposition (NNCPD) for data tensor.
result Significantly improved results compared to NMF-based methods.
Proposes HMHP for joint modeling of user-topic interactions.
problem Complex interactions between users, topics and time on social media.
method Hidden Markov Hawkes Process (HMHP) incorporating topical Markov Chains.
result HMHP outperforms state-of-the-art models in generalization and accuracy.
Detects changes in topic proportions over time in large text datasets.
problem Unsupervised detection of structural changes in topic distributions over time.
method Specialised temporal topic model with changepoint detection, approximate inference using sample splitting and likelihood ratio statistic.
result Automated detection of changepoints in topic proportions, facilitating interpretable results.
Study finds common poetic themes across languages over time.
problem Understanding thematic evolution in different poetic traditions.
method Applied Latent Dirichlet Allocation (LDA) to poetry corpora of four languages.
result Identified common themes and their temporal trends across poetic traditions.
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
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.
Predicts entity-wise topical behavior from network logs.
problem Predicting entity-wise topical behavior from network logs.
method Combines RNN and CNN learning architectures with reduction steps to form homogeneous topical metrics.
result Improves prediction accuracy with both temporal and spatial gains compared to MLP.
Topic model captures health journeys of multiple authors.
problem Challenges in topic modeling health journals due to asynchronous writing.
method Dynamic Author-Persona topic model (DAP) with regularized variational inference.
result Significant improvements over competing models, especially with regularization.
Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several mode…
We examine the problem of learning a probabilistic model for melody directly from musical sequences belonging to the same genre. This is a challenging task as one needs to capture not only the rich temporal structure evident in music, but also the complex statistical dependencies among different music components. To ad…
Proposes TEMN for better POI recommendations.
problem Challenges in capturing user preferences and spatio-temporal POI relationships.
method Integrates topic model and memory network, incorporating geographical module.
result Improves POI recommendation effectiveness by 3.25% and 29.95%.
This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s. Our study is based on 80,757 published journal abstracts from 37 of the leading OR/MS journals. We have developed a topic model, using Latent Dirichlet Al…
In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document level topic proportions with covariates and dynamic structure…
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.
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with e…
Proposes a new RNN for language generation capturing long-range dependencies.
problem Capturing long-range word dependencies and sentence order in text corpora.
method Recurrent Hierarchical Topic-Guided RNN with dynamic deep topic model.
result Outperforms larger-context RNN-based language models and learns interpretable topics.
DAPPER improves scalability of DAP topic model for large corpora.
problem Scaling complex models like DAP to large text corpora.
method Adapted approximate inference techniques for DAP, developing CVI-based EM.
result Significant improvements in model fit and training time without compromising structure.
New DTMs model text evolution with Gaussian processes and scalable inference.
problem Challenges in modeling text evolution with continuous stochastic processes.
method Extended tractable priors to Gaussian processes and developed scalable inference methods.
result Found interesting patterns in large-scale datasets not accessible before.
Novel hybrid model combines image feature discovery and topic modeling for marine robot mission data.
problem Insufficient analysis of robot mission data due to data complexity and lack of suitable models.
method Convolutional autoencoders for feature discovery in images and Bayesian nonparametric topic models for thematic structure.
result Hybrid model outperforms state-of-the-art approaches in unsupervised seafloor terrain characterization.
A new model for content diffusion over social media networks.
problem Modeling content diffusion with complex longitudinal dependency.
method Hierarchical Nonparametric Point Process (HNP3) with a mixture intensity function and a hierarchical dependent nonparametric approach.
result The proposed model effectively models content diffusion over networks.
Proposes tPARAFAC2 for tracking evolving patterns in time-evolving data.
problem Lack of temporal regularization in tensor factorizations for capturing evolving patterns.
method Temporal PARAFAC2 (tPARAFAC2) with temporal regularization.
result tPARAFAC2 accurately captures evolving patterns better than existing methods.
Quantum field theory explains machine learning symmetries.
problem Machine learning symmetries and convergence issues.
method Formulated a gauge theory of `charged' embedding vectors in time series models.
result Making the loss function gauge invariant speeds up convergence.
Compact model uses RBMs for sequence classification with fewer parameters.
problem Sequence classification with dynamic models and complex neural networks.
method Rolling RBMs over time for representation learning and temporal inference.
result Outperforms state-of-the-art models in melody and character recognition.
Paper proposes a new learning principle based on least cognitive action.
problem Learning from temporal data, especially with time involved.
method Introduces a new learning principle based on the principle of Least Cognitive Action, which is better suited for temporal learning.
result The new principle leads to a learning process driven by differential equations, similar to natural laws.
New framework detects policy changes in black-box DMS over time.
problem Lack of transparency in black-box decision making systems.
method Proposes temporal transparency, maps to time series changepoint detection, develops framework.
result Reveals policy changes in real-world DMS, including announced and unannounced.
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
DynamicGEM learns node representations for evolving graphs.
problem Learning node representations for dynamic graphs.
method State-of-the-art algorithms for dynamic graph embedding.
result Evaluation framework for various downstream tasks.
A new method for filling in missing traffic data improves accuracy over existing techniques.
problem Incomplete spatiotemporal traffic data.
method Low-rank autoregressive tensor completion (LATC) framework.
result LATC framework better captures spatiotemporal consistency and local consistency.
Proposes a new multi-layer model for topic distributions.
problem Leveraging deep structures for learning word distributions of topics.
method A multi-layer generative process on word distributions of topics, where each topic is drawn from a mixture of topics from the layer above.
result Discover interpretable topic hierarchies and improve topic models' accuracy and interpretability.
This work extends the options framework to learn at multiple temporal resolutions.
problem Autonomous creation of temporal abstractions from data in reinforcement learning.
method Developed a hierarchical option-critic architecture capable of learning at multiple temporal resolutions.
result Derived policy gradient theorems for a deep hierarchy of options.
Efficiently models correlated topics with topic embeddings.
problem High computational cost and poor scaling in correlated topic modeling.
method Compact topic embeddings and efficient inference in low-dimensional space.
result Handles larger model and data scales without sacrificing performance.
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the co…
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local top…
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.
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
Paper finds better words for topic models by reranking top words.
problem Top words in topic models are not always representative.
method Reranking words by considering marginal probability over every topic.
result Reranked top words are more representative of topics.
A new method for reinforcement learning scales errors without tuning.
problem Error scaling varies across reinforcement learning tasks and stages.
method A simple scaling mechanism for temporal-difference learning.
result The method effectively mitigates interference between learning tasks.
Graph-based multi-view model predicts trading volume movement from various sources.
problem Lack of comprehensive understanding of trading volume movement from different sources.
method Graph-based approach incorporating long-term, short-term, and sudden event information.
result Our method outperforms strong baselines by a large margin.
Modeling social network activity through user and topic interaction.
problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.
Predictive topic models retain only relevant terms for better prediction and topic coherence.
problem Misspecification of topic models leads to poor prediction and topic coherence.
method Uses supervisory signal to select vocabulary terms improving prediction performance.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
New metric correlates local topic quality with human judgments.
problem Evaluation of topic models focuses on global metrics, ignoring token-level assignments.
method Proposed a human evaluation task and automated metrics to assess local topic quality.
result Consistency metric correlates best with human judgments of local topic quality.
New metric R2 improves topic model evaluation.
problem Lack of standard cross-contextual evaluation metrics for topic modeling.
method Introduces R2 as a coefficient of determination for topic models. result Improves topic model evaluation by providing a standard metric.
TopicEq model generates equations and text from scientific papers.
problem Communicating ideas in scientific texts using both mathematics and text.
method Joint topic and equation generation model using correlated topic model and RNN.
result Joint model outperforms existing topic and equation models for scientific texts.