Model learns from millions of emojis for better sentiment analysis.
problem Limited NLP data for sentiment analysis.
method Extends distant supervision to noisy labels of 64 emojis.
result State-of-the-art performance on 8 datasets.
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
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48 results for “Distant supervision”
Efficiently use expert time in relation extraction by using distant supervision and active learning.
problem Scarcity of labeled data in relation extraction, especially with expert time constraints.
method Use distant supervision to label data and active learning to refine results with expert feedback.
result Improved relation extraction results with active learning from expert feedback.
Study on noise models for noisy labels in NLP.
problem Quality of noise models from noisy labels.
method Theoretical analysis and synthetic dataset creation.
result Expected error of noise models derived.
A new model improves relation extraction accuracy through relation-gated adversarial learning.
problem Relation extraction from sentences is challenging due to expensive human annotation and noisy distant supervision.
method Proposes relation-gated adversarial learning for relation extraction, extending domain adaptation methods.
result The model outperforms previous domain adaptation methods and improves accuracy of distance supervised relation extraction.
Paper proposes a novel model to improve n-ary cross-sentence relation extraction by addressing noisy data and non-consecutive sentences.
problem Noisy labeled data and non-consecutive sentences in n-ary cross-sentence relation extraction.
method Two-level agent reinforcement learning model and hybrid attention mechanism/PCNN approach.
result The model reduces the impact of noisy data and achieves better performance.
Novel unsupervised relation extraction framework using BERT.
problem Relation extraction without supervision.
method Syntactic parsing, pre-trained embeddings, distant supervision, fine-tuning BERT.
result Significantly outperforms baselines and matches state-of-the-art in three out of four data sets.
State-of-the-art Twitter sentiment analysis using CNNs and LSTMs.
problem Improving Twitter sentiment classification accuracy.
method Pre-trained word embeddings, distant supervision, fine-tuning, ensemble of CNNs and LSTMs.
result First rank on all five English subtasks of SemEval-2017.
Reduces false alarms in ICU monitoring systems.
problem High false alarm rates in ICU monitoring systems.
method Multitask learning with distant supervision.
result Significant improvements over state-of-the-art baselines.
Paper proposes a reinforcement learning framework for distant supervision of question parsing.
problem Lack of gold annotations for parsing in QA datasets.
method Reinforcement learning framework based on formal queries to learn mentions of entities and relations.
result Significant improvement in entity and relation linking compared to state of the art.
Proposes a method to predict stock movements using fine-grained events from finance news.
problem Lack of specific semantic information in coarse-grained events for stock movement prediction.
method Built a finance event dictionary, extracted fine-grained events, combined with stock trade data, and used distant supervision for training.
result Method outperforms all baselines and shows good generalizability.
Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a univer…
Models extract relevant EHR snippets to aid radiologists in diagnosis.
problem Difficulty in identifying relevant patient record information for diagnosis.
method Distantly supervised transformer-based neural model for extractive summarization.
result Models yield better extractive summaries than unsupervised approaches.
Paper tackles noisy relation classification by sentence-level reinforcement learning.
problem Noisy distant supervision in relation classification.
method Two-module approach: instance selector using reinforcement learning, relation classifier making sentence-level predictions.
result Jointly trained model optimizes instance selection and relation classification, effectively handling noisy data.
Prototypal analysis improves archetypal analysis by penalizing distant prototypes, making it more robust and interpretable.
problem Sensitivity to outliers and non-locality in archetypal analysis limit its applicability as a learning tool.
method Prototypal analysis finds prototypes through convex combination of data points, penalizing distant prototypes.
result Prototypal analysis is more robust and interpretable than archetypal analysis.
DERWENT learns paths for distant transfer learning via deep random walk.
problem Transfer learning between distant domains is challenging.
method DERWENT uses deep random walk to explicitly learn paths between source and target domains.
result DERWENT achieves state-of-the-art performance on benchmark datasets.
HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.
problem Classifying nodes in sparsely labeled graphs with limited labeled data.
method Hop-aware supervision mechanism and simulated annealing learning strategy.
result The model achieves high accuracy even with 40% labeled data, reducing performance loss to 3.9%.
Large receptive field CNNs improve distant speech recognition.
problem Degrading performance of ASR systems in noisy environments.
method Investigated large receptive field CNN variants including recursive, dilated, and hourglass networks.
result Stacked hourglass networks show significant improvements in distant speech recognition.
Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the po…
The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.
problem Improving the initialization of k-means clustering.
method Generalizing the center initialization process by selecting the most distant point from the nearest center.
result Choosing the most distant point from the nearest center achieves similar clustering quality to k-means++.
Quaternion neural networks improve distant speech recognition.
problem Challenges in distant speech recognition due to noise and reverberation.
method Quaternion neural networks process multi-channel audio signals as quaternion entities, capturing internal and external dependencies.
result QLSTM outperforms real-valued LSTM on multi-channel distant speech recognition tasks.
Neural network uses local credit assignment to learn distant cause-effect relationships.
problem Learning distant cause-effect relationships in sequential data.
method Sparse coding in a recurrent neural network memory with local and immediate credit assignment.
result Network can predict partially-observable higher-order sequences and navigate mazes.
A new dataset for few-shot relation classification challenges current models.
problem Few-shot relation classification is an open problem requiring further research.
method Adapted state-of-the-art few-shot learning methods for relation classification.
result Current models struggle with relation classification, especially compared to humans.
Proposes using knowledge graphs to improve probabilistic model estimation.
problem Lack of direct utilization of domain knowledge in regularization methods.
method Knowledge-based distant regularization framework using knowledge graph embeddings.
result Improvement in generalization capability of probabilistic models.
Study shows protein folding rate linked to topological changes.
problem Understanding protein folding kinetics and topology.
method Gauss linking integral, torsion, and sequence-distant contacts.
result Protein topology shifts from right-handed to left-handed with decreasing folding rate, associated with more sequence-distant contacts.
Paper calculates L-invariant and L*-invariant for complex surface sums.
problem Calculating invariants for complex surface sums.
method Using pants complexes and dual curve complexes.
result First example of arbitrary large invariants for bridge numbers.
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generationcs.LG
Hierarchical Foresight improves robot vision tasks by planning long-term goals.
problem Compounding uncertainty and scalability issues in long horizon video prediction.
method Subgoal generation and planning using hierarchical visual foresight (HVF).
result Achieves nearly 200% performance improvement in vision-based manipulation tasks.
New theorem connects distant points and identical points on manifolds.
problem Continuous maps and distant points on manifolds.
method Qualitative extension of Hopf theorem, using topological 'distant' points.
result Existence of connected component containing both distant and identical points.
Team QCRI-MIT detects hyperpartisan news with 72.9% accuracy.
problem Detecting hyperpartisan news from biased political content.
method Logistic regression model using engineered features from propaganda detection.
result Significant performance improvements with better feature pre-processing.
Loss assigns examples to classes and superclasses in hierarchical data.
problem Learning from hierarchical classification problems with known class hierarchy.
method Introduces a loss function that considers the hierarchy of classes, aiming for consistent classification across different granularities.
result Improves accuracy and reduces coarse errors in classification compared to cross-entropy loss.
New method for embedding large networks without attributes, achieving state-of-the-art performance.
problem Learning embeddings from large-scale networks without domain-dependent attributes.
method Use predefined local encodings based on node degree frequencies at different distances.
result Inductive network embeddings generalize well across unseen or distant regions in the network.
Curvature improves label space encoding for better class representation.
problem Inconsistent class distances in one-hot encoding.
method Introducing curvature using a metric tensor.
result Better representation of ancestral and convergent relationships.
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficultycs.CL
Paper proposes a method to predict EL difficulty using consensus-based labels.
problem Challenges in automatically identifying and resolving ambiguous entity mentions.
method Consensus-based method to generate difficulty labels, supervised classification with various features.
result EL difficulty can be accurately predicted with high accuracy.
A new MCMC method improves HMC for distant multi-modal sampling.
problem Sampling from multi-modal distributions, especially when modes are far apart.
method Improves Hamiltonian dynamics and uses variational distribution.
result The method can converge to target distributions effectively.
Method identifies credible medical statements from online health communities.
problem Inaccuracies and misinformation in user-generated medical resources.
method Probabilistic graphical model using linguistic cues and expert supervision.
result Successfully extracts rare or unknown side-effects of medical drugs.
Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.
problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.
3D RadViz improves 3D data visualization of multidimensional datasets.
problem Tackles the challenge of visualizing multidimensional datasets in 3D.
method Develops RadViz3D, a 3D radial visualization tool with uniform anchor points.
result Improves the display of multidimensional datasets, especially for uncorrelated variables.
A new method improves graph node embeddings by considering both nearby and distant node similarities.
problem Improving graph node embeddings by considering both nearby and distant node similarities.
method Distance-aware Negative Sampling (DNS) which maximizes cohesion at nearby node-pairs and separation at distant node-pairs.
result DNS outperforms baseline methods in downstream node classification tasks on various datasets and GRL algorithms.
New method estimates graph compatibility from sparse labels.
problem Estimating graph compatibility from sparse labeled data.
method Factorized graph representations and algebraic amplification.
result End-to-end classification accuracy comparable to gold standard.
Simple framework decouples word alignment and multilingual embedding mapping.
problem Learning multilingual embeddings without supervision.
method Two-stage approach: 1) unsupervised word alignment, 2) mapping embeddings to shared space.
result Robust performance across various multilingual tasks, including distant languages.
Improves neural machine translation by learning better source representations with relation networks.
problem Forgetting distant information and disregarding relationship between source words in neural machine translation.
method Introduces relation networks to learn better source representations by associating source words with each other and retaining their relationships.
result Significantly improves translation performance over conventional encoder-decoder models and outperforms approaches involving supervised syntactic knowledge.
Paper introduces new loss functions for Siamese networks using FDA.
problem Training Siamese networks with improved loss functions.
method Proposes Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC) loss functions based on FDA.
result Shows effectiveness of FDT and FDC on MNIST and histopathology datasets.
Main mathematical applications of Frobenius manifolds are in the theory of Gromov - Witten invariants, in singularity theory, in differential geometry of the orbit spaces of reflection groups and of their extensions, in the hamiltonian theory of integrable hierarchies. The theory of Frobenius manifolds establishes rema…
Proposes a method to improve graph embedding by removing least frequent nodes.
problem Capturing global graph structure in random walk-based embeddings.
method Extends random walk-based graph embedding by removing least frequent nodes.
result Improves predictive performance slightly, if at all.
Deep learning model for demographic classification using label proportions.
problem Lack of annotated training data for deep learning.
method LLP setting with Batch Averager regularization and co-training algorithm.
result Deep LLP model outperforms baselines for demographic classification.
Survey on negative transfer in machine learning.
problem Negative transfer in transfer learning reduces target domain performance.
method Systematic review of 50+ approaches to mitigate negative transfer.
result Lack of systematic survey on negative transfer.
MPNNs over-squash distant node information, study shows.
problem Over-squashing in MPNNs where node features ignore distant nodes.
method Theoretical analysis of MPNNs' over-squashing, focusing on width, depth, and graph topology.
result Width mitigates over-squashing but makes network more sensitive, depth doesn't help, graph topology is key.
Paper tackles treatment leakage in text-based causal inference, proposing methods to mitigate bias.
problem Treatment leakage in text-as-confounder applications introduces bias in causal estimates.
method Formal definitions, four text distillation methods (passage removal, classification, salient feature removal, nullspace projection).
result Moderate distillation optimally balances bias reduction against confounder retention.
Proposes MLCNN for better multivariate time series forecasting.
problem Challenges in forecasting multivariate time series, especially the limitation of predicting only one future moment.
method MLCNN, a multi-task deep learning framework inspired by Construal Level Theory, fuses future visions of near and distant future predictions.
result Significant improvements in forecasting accuracy (4.59% RMSE reduction, 6.87% MAE reduction) on real-world datasets.