Characterizes structures on generalized tangent bundles and CRF-structures.
problem Understanding structures on generalized tangent bundles and CRF-structures.
method Equivalent characterizations and spinor formalism for CRF-structures.
result Characterization of generalized complex manifolds as products and infinitesimal deformations of CRF-structures.
Study of quasi-classical generalized F and CRF structures.
problem Integrability conditions and properties of tensor fields.
method Analysis of tensor fields (A, π) and their relations.
result Established integrability conditions and properties of CRF structures.
We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function. While maximum likelihood is the most common approach to training CRFs, it ignores the inherent structure of the task's loss function. We describe alte…
A generalized F-structure is a complex, isotropic subbundle E of TcM⊕Tc∗M ($T_cM=TM\otimes_{\mathds{R}}\mathds{C}$ and the metric is defined by pairing) such that E∩Eˉ⊥=0. If E is also closed by the Courant bracket, E is a generalized CRF-structure. We show that a generalized F-structur…
CRF model improves protein secondary structure prediction.
problem Improving secondary structure prediction of proteins.
method Applied Conditional Random Fields (CRF) to protein classification.
result CRF model leads to extremely accurate protein secondary structure predictions.
NCRF transducers improve sequence labeling across tasks.
problem Sequence labeling with non-linear node potentials.
method Two RNNs: one for features and one for long-range label dependencies.
result Consistent improvements over linear-chain NCRFs and RNN transducers.
A new method reduces feature size in CRFs for faster training.
problem Challenges in solving sparse CRFs for large-scale applications.
method Safe dynamic screening method exploiting dual optimum estimation.
result Significant speedup in training CRFs without loss of accuracy.
Improved SAGA for CRFs reduces memory and improves training performance.
problem Training conditional random fields (CRFs) efficiently and accurately.
method Non-uniform sampling scheme for SAGA variant, reducing memory usage and improving convergence rate.
result Non-uniform SAGA outperforms existing methods in training objective and test error.
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact…
Efficiently infers fully-connected CRFs using low-rank approximation.
problem Inference on fully-connected CRFs is challenging due to long-range contextual relationships.
method Developed an efficient algorithm based on SDP and low-rank approximation of the similarity matrix.
result Demonstrated that the method can solve CRFs that were previously unsolvable.
FLDCRF improves sequence labeling performance with latent dynamics interactions.
problem Sequence labeling with improved performance and latent dynamics interactions.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) with multiple latent dynamics interactions.
result FLDCRF outperforms state-of-the-art models across multiple datasets.
Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of …
Hidden Markov Chains and Linear-chain CRFs are equivalent.
problem Comparing Hidden Markov Chains and Conditional Random Fields.
method Constructing an HMC with the same posterior distribution as a CRF.
result HMCs and linear-chain CRFs are equivalent models.
LC-CRFs are equivalent to HMMs, and MPM/MAP classifiers can be reformulated as CRFs.
problem Comparing and reformulating HMMs and CRFs.
method Demonstrating equivalence and reformulation of classifiers.
result LC-CRFs are equivalent to HMMs, and MPM/MAP classifiers can be reformulated as CRFs.
AC-RNN improves RNN for sequence labeling tasks.
problem RNN's exposure bias in maximum-likelihood training.
method Actor-Critic training for RNNs.
result AC-RNN outperforms CRF on NER and CCG tagging.
CAT is a new ASR toolkit using CRF and CTC for state-of-the-art speech recognition.
problem Improving automatic speech recognition systems.
method CRF-based discriminative training with CTC-inspired state topology.
result CAT achieves state-of-the-art results with fewer parameters and is competitive with hybrid models.
The paper improves CRFs for logical constraints in structured learning.
problem Structured learning with logical constraints in machine learning.
method General extension of CRF for logical constraints.
result Improved performance on a Document Understanding task.
Paper introduces combinatorial Ricci flows on infinite disk triangulations.
problem Discrete Ricci flows on infinite triangulations of the open disk.
method Combinatorial Ricci flows (CRFs) in Euclidean and hyperbolic geometries.
result Existence and uniqueness of CRFs, convergence results, uniformization theorem.
New algorithm improves CRF inference and learning.
problem Efficient inference and learning for dense CRFs.
method Regularized Frank-Wolfe algorithm for nonconvex CRF optimization.
result Regularized Frank-Wolfe outperforms mean field and CNN baselines.
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (S…
Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models is their great flexibility to include a wide array of overlapping, multi-granularity, non-independent features of the input. In face of thi…
Adapts SDCA for CRFs, improving performance on sequence prediction tasks.
problem Training conditional random fields (CRFs) efficiently.
method Adapts SDCA algorithm with adaptive sampling strategy for CRFs training.
result SDCA outperforms state-of-the-art methods on three out of four sequence prediction tasks.
Locally-contextual CRFs improve sequence labeling performance.
problem Improving sequence labeling with contextual embeddings.
method Locally-contextual nonlinear CRFs using deep neural networks.
result Consistently outperforms linear chain CRF and previous state of the art.
Deep neural networks improve music phrase segmentation.
problem Automated melodic phrase detection and segmentation in music.
method Adapted various neural network architectures to symbolic music representation, addressing sparse labeling problem.
result CNN-CRF architecture performs best, offering finer segmentation and faster training.
Enhances sequence labeling with embedded-state latent CRFs.
problem Complex non-local constraints between sequence labels.
method Integrates multiple hidden states with low-rank log-potential scoring matrices.
result Model outperforms baseline CRF+RNN models with global constraints.
Efficient deep learning for structured prediction using CNN message estimators.
problem Structured prediction with CRFs using expensive potential function calculations.
method Use CNNs to estimate messages in message passing inference for CRFs, reducing computational cost.
result Achieved best reported intersection-over-union score of 73.4 on PASCAL VOC 2012 test set.
New CRF model segments music into chords with rich features.
problem Harmonic analysis of music segments.
method Semi-Markov Conditional Random Field (semi-CRF) with segment-level features.
result Semi-CRF model performs better than previous approaches.
CAT toolkit combines hybrid and E2E approaches for efficient speech recognition.
problem Improving speech recognition efficiency and latency.
method CTC-CRF based framework with contextualized soft forgetting.
result CAT achieves state-of-the-art results with simpler training and streaming ASR.
Paper proposes a streamlined approach to clinical concept extraction using LSTM-CRF.
problem Automated extraction of concepts from clinical records for clinical research.
method Bidirectional LSTM with CRF decoding initialized with general-purpose word embeddings.
result Experimental results outperform all recent methods and rank closely to the best submission from the original i2b2/VA challenge.
New Polish word embeddings improve temporal expression recognition.
problem Recognizing temporal expressions in Polish text.
method Created KGR10 corpus, used BiLSTM-CRF model with custom embeddings.
result Custom embeddings enhance BiLSTM-CRF model's performance.
End-to-end sequence labeling system using LSTM-CNN-CRF.
problem Traditional sequence labeling systems require manual feature engineering and data preprocessing.
method Bi-directional LSTM-CNN-CRF architecture for neutral network.
result State-of-the-art performance on POS tagging and NER tasks.
The article confirms Thurston's conjecture for a specific class of 3-manifolds using combinatorial Ricci flow.
problem Thurston's triangulation conjecture for hyperbolic 3-manifolds.
method Combinatorial Ricci flow (CRF) with specific conditions and techniques to handle intrinsic difficulties.
result A class of 3-manifolds admits a unique complete hyperbolic metric with totally geodesic boundary.
Unsupervised RNNGs perform similarly to supervised ones in language modeling and grammar induction.
problem Training RNNGs requires annotated data, which is costly.
method Amortized variational inference with a neural CRF parser.
result Unsupervised RNNGs achieve comparable performance to supervised ones.
Paper combines latent state space with CRF for improved autoregressive text generation.
problem Autoregressive models expose hidden state trajectory to biases.
method Combines latent state space model with CRF observation model.
result Improved performance on unconditional sentence generation compared to RNN and GAN baselines.
FGN improves Chinese NER by integrating glyph information and interactive context.
problem Chinese named entity recognition is challenging due to the complexity of characters and their glyphs.
method FGN uses a novel CGS-CNN structure to capture glyph and interactive information, and a sliding window method to fuse BERT and glyph representations.
result FGN achieves state-of-the-art performance on four NER datasets, improving over previous methods.
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compute. A particular instance of interest is the L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a popular class of …
Improved SAR in asynchronous conversations using neural models and unlabeled data.
problem Lack of labeled data for SAR in asynchronous conversations.
method Hierarchical LSTM-CRF model, semi-supervised learning with word embeddings, adversarial training.
result Adversarial training improves SAR performance by leveraging labeled data from synchronous domains.
Efficiently improves non-autoregressive sequence models for better translation performance.
problem Heavy inference latency and inconsistent output sentences in non-autoregressive models.
method Incorporates a structured inference module with an efficient CRF approximation and dynamic transition technique.
result Significantly better translation performance (BLEU score 26.80) compared to previous non-autoregressive models.
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built…
Optimizes submodular extensions for efficient marginal estimation.
problem Efficiently compute approximate marginals for submodular energy functions.
method Equivalence between submodular extensions and LP relaxations for MAP estimation; worst-case optimality established.
result Worst-case optimal submodular extension for various models.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.
problem Imbalanced classification where one class is underrepresented.
method Theoretical analysis of Centered Random Forests (CRF) with rebalanced datasets and debiasing techniques.
result Theoretical Central Limit Theorem (CLT) for the infinite CRF and debiased estimator IS-ICRF.
Scrubbing PHI data from medical records is now efficient and scalable with SpaCy.
problem Efficiency and scalability of de-identification techniques for PHI data.
method Evaluated numerous deep learning techniques including SpaCy for performance and efficiency.
result SpaCy model is both well performing and extremely efficient for PHI data scrubbing.
Alternative optimizer outperforms gradient descent in weakly-supervised CNN segmentation.
problem Training deep neural networks with complex loss functions.
method Demonstrated an alternative optimizer (ADM) outperforming gradient descent.
result Gradient descent performs poorly with certain loss functions, while an alternative optimizer achieves state-of-the-art results.
Graph ConvNet improves classification by leveraging label graph structure.
problem Ignoring label graph structure in multi-class classification leads to suboptimal performance.
method Proposes a GCN-based neural network classifier that incorporates the graph structure of labels.
result The proposed model outperforms baseline methods in terms of graph-theoretic metrics.
Method uses random forest with distance covariance for transfer learning in healthcare.
problem Transfer learning in random forests with sparse differences between source and target.
method Distance covariance-based feature weights in residual random forest.
result Upper bound on mean square error rate for transfer learning in RF.
Proposes a deep model for geometric matrix completion.
problem Matrix completion from limited observations in recommender systems.
method Deep Conditional Random Fields (CRF) with end-to-end learning.
result Superior performance compared to state-of-the-art models.
We apply mixup regularization to RNNs for improved performance.
problem Improving the performance of Recurrent Neural Networks.
method We extend mixup regularization techniques to RNNs, making them easier and less computationally intensive.
result Sequence mixup improved F-1 score and reduced loss in Named Entity Recognition tasks.