Method extracts weighted automata from RNNs using state space regression.
problem Extracting weighted automata from RNNs for better model understanding.
method Regression on RNN state space to prioritize counterexample candidates.
result Quantitative/weighted extension of DFA extraction.
AxCell automates results extraction from machine learning papers.
problem Difficulty in tracking progress in machine learning due to many papers.
method AxCell uses table segmentation and learns structural knowledge to extract results.
result AxCell significantly improves results extraction compared to existing methods.
TXtract extracts structured knowledge from thousands of product categories.
problem Extracting structured knowledge from diverse product categories in e-commerce.
method TXtract uses a taxonomy-aware model with category conditional self-attention and multi-task learning.
result TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
Neural networks enhance relation extraction from biomedical literature.
problem Automated extraction of relations between biomedical concepts.
method Use of multichannel architectures in deep neural networks with biomedical ontologies.
result State-of-the-art results in relation extraction tasks.
Study optimal extraction from a finite resource under fluctuating market conditions.
problem Maximizing discounted cash flow from an exhaustible resource with fluctuating market prices.
method Singular stochastic control problem with regime switching, providing expressions for value function and optimal control.
result Optimal extraction rules depend on the convexity of maintenance costs and the level of commodity price.
VarDetect monitors API queries to detect model extraction attacks.
problem Attackers can extract ML models through API queries.
method VarDetect uses a modified variational autoencoder to track and detect attacker samples.
result VarDetect successfully detects and raises alarms for model extraction attacks.
New model improves DDI extraction with 78.5% macro F1-score.
problem Extracting Drug-Drug Interactions from text.
method Attention-Wrapped Hierarchical BLSTMs.
result Model outperforms state-of-the-art with macro F1-score of 0.785.
Neural Physicist learns physical dynamics from images.
problem Learning meaningful physical state representations and accurate state transitions from image sequences.
method Neural Physicist uses VAE for state extraction, NP for parameters, and SSM for dynamics.
result Achieves long-term predictions and identifies system degrees of freedom.
New method extracts high-accuracy, high-fidelity neural networks.
problem Extracting accurate and functionally-equivalent neural networks.
method Learning-based attack with practical functionally-equivalent extraction.
result First practical functionally-equivalent extraction attack.
Combines multiple data types to predict emotions in images.
problem Predicting emotions in images using various data types.
method Combines facial features, scene extraction, audio tonality, human pose, text-based tagging, and CNN predictions.
result Improves accuracy in emotion prediction compared to baseline methods.
A new method extracts features from time series data using iterated sums and improves classification accuracy.
problem Time series classification challenges.
method Feature extraction using iterated-sums signature (ISS) followed by a linear classifier.
result Competitive with state-of-the-art methods on UCR archive.
This work tackles extractive compression by formulating it as tree transduction.
problem Extractive compression as a challenging natural language processing problem.
method Formulated as a parse tree transduction problem, using a deep neural model with Long Short-Term Memory extended to consider parent-child relationships.
result Achieves state-of-the-art performance on sentence compression benchmarks.
GenIE extracts structured text with fewer errors and more entities.
problem Closed information extraction with errors and limited entities.
method End-to-end autoregressive generation with bi-level constraints.
result State-of-the-art performance on closed information extraction.
This review compares extractive and abstractive summarization methods.
problem Improving abstractive summarization in natural language processing.
method Compared various approaches including supervised and unsupervised methods, deep learning, and NLP.
result Current research uses combinations of approaches, but abstractive summarization remains unsolved.
Paper uses AC-GAN to generate high-quality relational sentences for relation extraction.
problem Limited training data for relation extraction models.
method Auxiliary Classifier Generative Adversarial Networks (AC-GANs).
result Significantly improved performance of relation extraction.
Model credit risk with hidden states for default times.
problem Credit risk assessment with complex default intensity functions.
method Hidden Markov process for state extraction, filtering method.
result Closed-form formulas for default times distribution.
DeepPainter classifies painters using autoencoders without manual feature extraction.
problem Painter classification problem.
method Deep convolutional autoencoder neural networks trained on raw pixel data.
result Significant improvement in classification accuracy (63% reduction in error rate).
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.
Paper proposes nearly unsupervised hashcode learning for relation extraction.
problem Relation extraction from text, especially in biomedical domains.
method Optimized hashcode representations learned from data points without class labels, followed by supervised classification.
result Significant accuracy improvements over state-of-the-art methods.
Proposes OpenKI for better web-scale knowledge extraction and alignment.
problem Combining OpenIE and KB for web-scale knowledge extraction and alignment.
method Instance-level inference using neighborhood information from KB and OpenIE extractions, with attention mechanisms.
result Significantly improves performance on OpenIE extractions and semi-structured data.
New method improves robot learning from vision with better sample efficiency.
problem Scaling reinforcement learning to real robots from vision.
method State representation learning to extract relevant features.
result Improved sample efficiency and robustness to hyper-parameters.
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.
MR-GNN predicts interactions between structured entities using multi-resolution and dual graph neural networks.
problem Predicting interactions between structured entities, especially considering features in substructures of different sizes and interactions between entities.
method MR-GNN uses a multi-resolution architecture and dual graph-state L-STMs to extract features from different neighborhoods and pairwise graphs, respectively.
result MR-GNN improves prediction accuracy compared to state-of-the-art methods.
End-to-end sound event detection uses learned time-frequency representations.
problem Sound event detection using standard features like mel spectrogram.
method End-to-end approach with a feedforward layer block and convolutional recurrent network.
result The learned time-frequency representations improve performance over fixed features.
State-regularized RNNs improve interpretability and performance on long-term memory tasks.
problem RNNs struggle with long-term memory and lack of interpretability.
method Introduce a stochastic state transition mechanism to limit state transitions to a finite set.
result State-regularized RNNs perform better on tasks requiring long-term memory.
ELICA helps analysts extract relevant information during elicitation meetings.
problem Challenges in eliciting requirements due to analyst's lack of domain knowledge.
method ELICA uses a novel information extraction algorithm combining WFSTs and SVMs, presented in an interactive GUI.
result ELICA effectively extracts relevant information in real-time and facilitates interactive and dynamic process.
Enhances feature extraction for sequential data using CNN and RNN.
problem Extracting features from sequential data efficiently.
method Integrates recurrent neural networks into convolutional layers for sequential data.
result Improves performance in audio classification tasks.
Method extracts knowledge from LSTM for sequence validation.
problem Validating sequences generated by unknown automata.
method Clustering hidden states to build a corresponding automaton.
result Automaton accurately predicts sequence validity.
Automatically extracts features from time series data for improved forecasting.
problem Manual feature selection for time series forecasting is inefficient and prone to errors.
method Extracts features from time series using recurrence plots and computer vision algorithms.
result Automatically extracted features lead to highly comparable and sometimes superior forecasting performance.
Deep learning improves handwriting style transfer and extraction.
problem Improving handwriting style transfer and extraction using deep neural networks.
method Used a deep conditioned autoencoder on IRON-OFF handwriting data-set to explore style transfer and extraction.
result Improved metrics of state-of-the-art methods by a large margin in style transfer and extraction experiments.
Novel method extracts hierarchical brain connectivity patterns from fMRI.
problem Functional hierarchical organization of the human brain.
method Sparse Connectivity Patterns (SCPs) with hierarchy of sparse overlapping patterns, deep factorization of correlation matrices.
result Reproducible multi-scale hierarchical SCPs more stable than single-scale patterns.
CURE extracts relations without supervision by clustering similar entity pairs.
problem Extracting relations unsupervised without considering sentence correlations.
method CURE uses Encoder-Decoder architecture for self-supervised learning and clustering similar relations.
result CURE outperforms state-of-the-art models on NYT and UNPC datasets.
Improves demand forecasting accuracy through recurrent transform learning.
problem Challenging task of building demand forecasting.
method Developed two versions of recurrent transform learning (RTL and R2TL) for feature extraction and regression.
result Both RTL and R2TL techniques outperform state-of-the-art methods.
Paper improves relation extraction in clinical texts with limited data.
problem Relation extraction in narrow knowledge domains with scarce annotated data.
method Introduces a bag-of-concepts (BoC) model and compares it with window-bounded co-occurrence (WBC).
result BoC model outperforms baseline and other complex methods on small dataset.
SPEDER extracts state-action abstraction from dynamics for reinforcement learning.
problem Curse of dimensionality and limited applicability of spectral methods.
method Spectral Decomposition Representation (SPEDER) that extracts state-action abstraction from dynamics without policy dependence.
result Theoretical analysis establishes sample efficiency in online and offline settings.
FIRE extracts interpretable rules from tree ensembles.
problem Building sparse and interpretable rule sets from tree ensembles.
method Optimization-based framework with fusion regularization and sparsity-inducing penalties.
result FIRE outperforms state-of-the-art rule ensemble algorithms.
Convolutional neural networks for graphs improve feature extraction efficiency.
problem Learning from graph data, especially for complex attributes.
method Proposed a framework for learning convolutional neural networks on arbitrary graphs.
result Learned feature representations are competitive with graph kernels and efficient.
Modeling cascading behavior in complex systems using CTBNs.
problem Understanding which states trigger cascading events in complex systems.
method Continuous-time Bayesian networks (CTBNs) for modeling and identifying likely sentry states.
result Identification of likely sentry states that may lead to cascading behavior.
A new method extracts events and their arguments efficiently from text.
problem Efficiently extract event information from texts with long-range dependencies and associations.
method Graph Convolutional Networks with shortest dependency paths to capture syntactic relationships.
result Significant improvement over state-of-the-art methods.
Automatically mined rules from dependency parsing help neural models learn from less labeled data.
problem Lack of labeled data for aspect and opinion term extraction.
method Automatically mined rules from dependency parsing, applied to auxiliary data, combined with human-annotated data.
result Neural models achieve better performance than state-of-the-art with mined rules and auxiliary data.
A method uses image processing and deep learning for financial market state prediction.
problem Low signal-to-noise ratio in financial time series data.
method Wavelet transform for denoising, convolutional neural network for pattern extraction.
result Competitive prediction accuracy of market states 'Up' and 'Down' on S&P 500 data.
Model predicts epileptic seizures by detecting preictal state using wavelet transform and PCA.
problem Predicting epileptic seizures before onset.
method Common spatial pattern filtering, wavelet transform for preprocessing, PCA for feature extraction, SVM for classification.
result Average sensitivity of 93.1% for 84 seizures in 23 subjects.
DARE uses GPT-2 to generate relation examples, improving RE performance.
problem Challenges in Relation Extraction, especially limited data and class imbalance.
method Data augmentation with GPT-2 fine-tuning to generate relation examples.
result Improves RE performance by up to 11 F1 score points.
Extracts StarCraft II tournament data for AI and ML studies.
problem Lack of accessible esports data for scientific use.
method Gathered and processed StarCraft II tournament replays using an API parser library.
result The largest publicly available StarCraft II esports dataset.
New method extracts high-quality saliency maps without classifier dependence.
problem Fixed classifier dependence limits saliency map extraction performance.
method Classifier-agnostic saliency map extraction.
result Sets new state of the art for localization tasks on ImageNet.
Bayesian filtering approach identifies nonlinear restoring forces in dynamic systems.
problem Identification of nonlinear dynamic systems in engineering.
method Modeling the nonlinear restoring force as a Gaussian process, converting it to a state-space model, and inferring internal states and the nonlinear restoring force through filtering and smoothing.
result The approach effectively identifies nonlinear restoring forces in both simulated and experimental datasets.
Research evaluates model extraction attacks on complex ML models and introduces a defense.
problem Model extraction attacks steal functionality of ML models through prediction APIs.
method Evaluation of Knockoff nets and introduction of a defense.
result Realistic adversaries can effectively steal complex ML models and evade known defenses.
Extract rules from LSTMs for better understanding of natural language processing models.
problem LSTMs are black boxes; understanding their decision-making process is difficult.
method Identify important patterns of words in LSTMs for specific outputs.
result Extracted phrases can approximate LSTM outputs in simple rule-based classifiers.