Hand-held system translates foreign menus for diet management.
problem Translation ambiguities and context-specific information for diet management.
method Portable multimedia device, machine translation, context-specific corpora, pre-processing steps, multimedia information.
result Higher accuracy and instant translations compared to Google Translate.
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
problem Modeling context-specific causal relationships from mixed data types.
method Introduces CStrees with a novel factorization criterion and graphical characterization for context-specific conditional independence models.
result Derives a graphical characterization of model equivalence for observational CStrees and extends it to CStree models under context-specific interventions.
NOTMAD estimates context-specific Bayesian networks without breaking datasets.
problem Non-convexity of acyclic graphs limits sharing information between context-specific estimators.
method NOTMAD models context-specific Bayesian networks as mixtures of archetypal DAGs, estimating structures and parameters jointly.
result NOTMAD shares information between context-specific acyclic graphs, enabling single-sample resolution.
Scalable method learns context-specific models for hundreds of variables.
problem Learning context-specific models for large numbers of variables.
method Order-based Markov chain Monte-Carlo search with context-specific sparsity assumption.
result Method scales to hundreds of variables and learns accurate models.
Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific ind…
New classifiers account for context-specific independences.
problem Restrictions in generative models for classification.
method Staged tree classifiers that account for context-specific independences.
result Staged tree classifiers achieve competitive classification accuracy.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
problem Interpreting dynamic human decision-making processes in medical contexts.
method Develops Contextualized Policy Recovery (CPR) framework for multi-task learning, modeling each context-specific policy as a linear map.
result Achieves state-of-the-art performance in predicting medical decisions, closing the gap between interpretable and black-box methods.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for mining such data from previously obtained comparable corpora. The task is highly practical since non-para…
We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora. Many SC frameworks require parallel corpora, phonetic alignments, or explicit frame-wise correspondence for learning conversion functions or for synthesizing a target spectrum with the aid of alignments. Howev…
Modeling lead-lag relationship between two text corpora for improved topic modeling.
problem Recognizing the relationship between multiple text corpora for better topic modeling.
method Proposed a jointly dynamic topic model and embedding extension for large-scale text corpus.
result The proposed model can well recognize the lead-lag relationship between two text corpora and improve topic learning.
CausalRegNet generates accurate data for gene perturbation experiments, improving CSL methods.
problem Assessing and selecting causal structure learning methods in gene perturbation experiments.
method CausalRegNet, a multiplicative effect structural causal model, generates accurate observational and interventional data.
result CausalRegNet generates more accurate distributions and scales better than current simulation frameworks.
Parallel texts are a relatively rare language resource, however, they constitute a very useful research material with a wide range of applications. This study presents and analyses new methodologies we developed for obtaining such data from previously built comparable corpora. The methodologies are automatic and unsupe…
We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of text that is designed to analyze corpora composed of documents in two languages. From these documents, MuTo uses stochastic EM to simultaneously discover both a matching between the languages and multilingual latent topics. We d…
A new HMM model captures kernel dependencies using context-specific Bayesian networks.
problem Traditional HMMs struggle with non-Gaussian data and independence assumptions.
method Kernel density estimation with context-specific Bayesian networks.
result The proposed model outperforms related HMMs in likelihood and classification accuracy.
Efficiently trains large corpora models without sampling.
problem Training neural network embedding models on very large corpora using SGD is expensive.
method Proposes new methods to train models without sampling unobserved pairs, using Gramian estimation and variance reduction schemes.
result Significant improvement in training time and generalization quality compared to traditional methods.
Study shows context-specific models improve swipe gesture authentication for smartphone users.
problem Improving swipe gesture-based continuous authentication for smartphones.
method Conducted experiments on HMOG dataset with 100 subjects, analyzing authentication error in different scenarios.
result Context-specific models are needed for different smartphone usage and human activity scenarios.
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based learning. Such approach guarantees the learning of the correct structure …
Pref-SHAP explains preferences using Shapley values.
problem Challenging problem of preference explanation in machine learning.
method Shapley value-based model explanation framework for pairwise comparison data.
result Richer and more insightful explanations obtained over baseline.
Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.
problem Efficiently searching over large natural language corpora with semantic variations.
method String matching based on suffix arrays, vector representation of words, dynamic corpus-aware pruning, fast exact lookup.
result Substantially lower search latency compared to existing methods on FineWeb-Edu corpus.
Paper proposes continual learning for sentence encoders.
problem Optimize sentence encoders for new corpora while maintaining old corpus accuracy.
method Initialize encoders with corpus-independent features, update using Boolean operations of conceptor matrices.
result Proposed sentence encoder can continually learn features from new corpora.
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.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel m…
Improves topic modeling for multi-collection corpora.
problem Challenges in text mining from multi-collection corpora.
method Compound Latent Dirichlet Allocation (cLDA) model and MCMC methods.
result cLDA model identifies topic proportions across multiple collections.
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network …
Improved neural NER by optimizing large corpora for German.
problem Low-resource language named entity recognition.
method Optimized large corpora, lemmatization, part-of-speech tagging, and detailed optimization.
result Up to 11% improvement in F-score on German NER tasks.
This paper proposes an efficient method to train word embeddings for large corpora without synchronization.
problem Training word embeddings for large text corpora is computationally expensive and requires synchronization.
method Partition the input space instead of the vocabulary size, using asynchronous training without parameter synchronization.
result Comparable and up to 45% performance improvement in NLP benchmarks with 1/10 the training time.
The paper introduces new Bayesian network classifiers for better classification accuracy.
problem Improving supervised classification accuracy using Bayesian network classifiers.
method Developed novel classes of generative classifiers based on staged tree models, extending Bayesian networks.
result Data-driven learning routines enhance the accuracy of the new classifiers.
In this paper, we attempt to improve Statistical Machine Translation (SMT) systems on a very diverse set of language pairs (in both directions): Czech - English, Vietnamese - English, French - English and German - English. To accomplish this, we performed translation model training, created adaptations of training sett…
Bayesian model identifies three types of travelers adapting to feedback.
problem Capturing adaptive, feedback-driven travel behavior in heterogeneous individuals.
method Latent Class Reinforcement Learning (LCRL) model with Variational Bayes estimation.
result Three distinct traveler classes identified: context-dependent, persistent exploitative, and exploratory.
Two methods generate parallel data for GEC, improving neural models' performance.
problem Lack of parallel data for GEC.
method Two approaches to generate large parallel datasets from Wikipedia data.
result Neural GEC models trained on generated corpora perform similarly and surpass state-of-the-art.
Neural topic models improve topic discovery in large corpora.
problem Topic discovery in large text corpora.
method Neural auto-encoder based document embedding with PLSA assumptions.
result Neural topic models outperform LDA in capturing relevant topics and perplexity.
Develops N Time-Slice DCEG for analyzing inmate radicalization.
problem Limited application of DCEG due to its generality.
method Object-oriented method to analyze NT-DCEG and Markov processes.
result Context-specific independence statements from graph topology.
A new clustering method using autoencoders for improved data representation.
problem Improving clustering of complex data like images and text.
method DAMIC algorithm based on a mixture of deep autoencoders.
result Significant improvement over state-of-the-art methods on image and text corpora.
The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have b…
This work tackles domain shift in speech emotion recognition by proposing class-wise adversarial domain adaptation.
problem Domain shift between corpora poses a challenge for speech emotion recognition, especially for positive/negative emotions.
method Class-wise adversarial domain adaptation to reduce shift between different corpora.
result Our method is effective even with limited target labeled examples, as demonstrated on EMODB and Aibo corpora.
Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc. Despite of their generic applicability and wide adoptan…
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the…
The paper creates a language evolution tree using word vectors from historical novels.
problem Exploring the evolution of language through historical texts.
method Constructed word vectors from novels, combined them, and used hierarchical clustering.
result Discovered a specific language evolution tree that reflects the year of the corpus.
We study the problem of topic modeling in corpora whose documents are organized in a multi-level hierarchy. We explore a parametric approach to this problem, assuming that the number of topics is known or can be estimated by cross-validation. The models we consider can be viewed as special (finite-dimensional) instance…
NT-DCEG models dynamic processes with specific periodicity, proving useful for multivariate processes.
problem Modeling dynamic processes with specific periodicity and context-specific independences.
method Developed a method to distributively construct NT-DCEG models and used graph topology to infer context-specific independences.
result NT-DCEGs contain all discrete N time-slice Dynamic Bayesian Networks as special cases.
Paper proposes set-valued prediction for historical POS tagging.
problem Difficult POS tagging in historical corpora due to lack of native speakers and sparse data.
method Set-valued prediction approach to allow uncertainty in tagging.
result Set-valued prediction improves POS tagging precision and robustness.
Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-…
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several co…
QA-Token improves tokenization for noisy data, boosting model performance.
problem Tokenization ignores data quality, limiting model effectiveness on noisy corpora.
method QA-Token combines signal quality with vocabulary construction through bilevel optimization and reinforcement learning.
result QA-Token achieves state-of-the-art performance on genomic and financial datasets.
Unsupervised segmentation learns features without labels, improving accuracy.
problem Discover and localize semantically meaningful categories in images without annotations.
method Separates feature learning from cluster compactification; distills unsupervised features into discrete semantic labels using a contrastive loss function.
result Significant improvement over prior state of the art on semantic segmentation challenges.
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.
Model learns multilingual word representations robust to noise.
problem Learning multilingual word representations in noisy environments.
method Fit a generative latent variable model to a multilingual dictionary.
result Competitive multilingual embeddings across various tasks.
Competency questions help experts select best clustering for energy data.
problem Ad hoc and subjective selection of clustering structures by domain experts.
method Formalize expert knowledge and requirements with competency questions.
result Competency questions improve reproducibility and evaluation of clustering applications.