Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a re…
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48 results for “recurrent method”
Trains recurrent networks to perform complex tasks with fewer neurons and better robustness.
problem Training recurrent networks to handle temporally complex tasks efficiently.
method Introduces a target network to provide suitable dynamics for the task, modifying the full connectivity matrix.
result Trained networks perform tasks with fewer neurons and greater noise robustness.
CRAN extracts music highlights using attention and recurrent layers.
problem Extracting valuable music highlights from signals.
method Convolutional Recurrent Attention Networks (CRAN) with attention mechanism.
result CRAN outperforms three baseline methods in highlighting extraction.
This work introduces a new model for complex stochastic processes.
problem Difficulties in representing non-stationary distributions with conventional models.
method Recurrent Autoregressive Flows using normalizing flows with recurrent neural connections.
result Demonstrates the effectiveness of the proposed model through experiments.
Recurrent neural networks improve time series forecasting accuracy.
problem Time series forecasting is challenging, especially for sequential data.
method A recurrent neural network framework for feature engineering, prediction, and evaluation is presented.
result The LSTM and GRU networks outperform traditional methods in forecasting accuracy.
New methods improve Reservoir Computing for chaotic time series prediction.
problem Chaotic time series prediction in Reservoir Computing.
method Established Recurrent Kernel limit, introduced Structured Reservoir Computing.
result Structured Reservoir Computing is faster and more memory-efficient.
Enhanced speech emotion recognition using nonlinear recurrence dynamics.
problem Improving speech emotion recognition accuracy.
method Phase space reconstruction, Recurrence Plot, Recurrence Quantification Analysis, statistical functionals, feature fusion, Bidirectional Recurrent Neural Network.
result State-of-the-art performance on IEMOCAP with up to 10.7% improvement in accuracy.
Study compares LSTM, Transformer, and Mamba for bladder cancer recurrence analysis.
problem Complex time-dependent data in bladder cancer recurrence analysis.
method Evaluation of LSTM, Transformer, and Mamba models using Cox proportional hazards model.
result LSTM-Cox model outperforms Transformer-Cox and Mamba-Cox models in prediction accuracy.
New method improves learning of long-term dependencies in recurrent models.
problem Improving learning of long-term dependencies in recurrent neural networks.
method Proves learnable gates in recurrent models provide quasi-invariance to time transformations and recovers part of LSTM architecture from axiomatic approach.
result New chrono initialization of gate biases greatly improves learning of long-term dependencies.
ENRNN uses eigenvalue normalization for short-term memory in RNNs.
problem Vanishing/exploding gradient problem and long-term dependency modeling.
method Eigenvalue normalization of recurrent matrix to simulate short-term memory.
result ENRNN outperforms existing RNN variants in experiments.
Scalable verifier for recurrent neural networks using polyhedral abstractions.
problem Certifying the correctness of recurrent neural networks.
method Combining sampling, optimization, and Fermat's theorem for polyhedral abstractions; gradient descent for refinement.
result Successfully verified challenging recurrent models in various domains.
A method to construct fractal surfaces by recurrent fractal curves is provided. First we construct fractal interpolation curves using a recurrent iterated functions system(RIFS) with function scaling factors and estimate their box-counting dimension. Then we present a method of construction of wider class of fractal su…
Study on different types of recurrence in Finsler geometry.
problem Understanding various recurrence patterns in Finsler geometry.
method Adopted pullback approach to investigate three classes of recurrence: simple, Ricci, and concircular.
result Introduce and investigate four types of each class of recurrence, highlighting interrelationships and a new concept of generalized concircular recurrence.
SIS-RNN improves model flexibility for sequential data.
problem Limited expressive power of existing stochastic RNNs.
method Semi-implicit variational inference for implicit latent representations.
result SIS-RNN outperforms existing methods in various tasks.
Causal inference for the expected number of recurrent events in the presence of a terminal eventstat.ME
Develops a method for causal inference in recurrent event data with terminal failure.
problem Causal inference in recurrent event data with a terminal event.
method Multiply robust estimation framework for causal inference.
result Proposes an estimator for the expected number of recurrent events and failure survival function.
Recurrent models can produce infinite sequences, causing bias; new methods prevent this.
problem Inconsistency in decoding infinite-length sequences from recurrent language models.
method Defined and proved inconsistency of common decoding algorithms; proposed remedies.
result Proposed methods prevent inconsistency in practice.
This work optimizes reservoir computing models by linking recurrence and non-linear dynamics.
problem Understanding how recurrence and non-linear dynamics in cortical networks contribute to their function.
method Transformed time-continuous, recurrent dynamics into an effective feed-forward structure of linear and non-linear temporal kernels.
result Optimal time-series classifiers can be built from random reservoir networks, demonstrating significant performance gains.
This work combines recurrent models with diffusion for probabilistic time series forecasting.
problem Scalability and capturing high-dimensional distributions and cross-feature dependencies in time series forecasting.
method Combines recurrent neural networks' efficiency with diffusion models' probabilistic modeling, using stochastic interpolants and conditional generation.
result Offers scalable probabilistic time series forecasting methods.
Investigates new types of recurrence in Finsler geometry.
problem None explicitly stated in the abstract.
method Study of hyper-generalized recurrence and generalized conharmonic recurrence in Finsler geometry.
result Properties of hyper-generalized recurrence and generalized conharmonic recurrence are studied and their relations to other Finsler recurrences are explored.
A new batch construction method for RNNs outperforms existing approaches in MXNet.
problem Improving the efficiency and performance of recurrent neural networks in MXNet.
method Proposes an alternately sorted batch construction strategy for RNNs.
result Alternately sorted batches outperform bucketing and other methods in training time and recognition performance.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
problem Improving binary classification accuracy through ensemble methods.
method RCAM-based ensemble combining classifiers using similarity and recurrent consult-vote scheme.
result RCAM-based ensemble outperforms individual classifiers and majority voting.
New method selects facts in proofs using stateful recurrent neural networks.
problem Selecting facts for proving new goals over large formal libraries.
method Stateful architecture based on recurrent neural networks with data augmentation.
result Significantly better performance and solving many new problems compared to previous methods.
SnAp approximates RTRL for online training of sparse recurrent networks.
problem Training large sparse recurrent networks online is computationally expensive.
method Sparse n-step Approximation (SnAp) of the RTRL influence matrix.
result SnAp with n=2 remains tractable for highly sparse networks and outperforms backpropagation through time.
CNN-RNNs detect bird sounds with high accuracy.
problem Automated detection of bird sounds in varied environments.
method Convolutional Recurrent Neural Networks (CNN-RNNs) for feature extraction and dependency capture.
result 88.5% AUC score on unseen data.
Recurrent Kernel Networksstat.ML
End-to-end training of recurrent neural networks for biological sequences.
problem Training recurrent neural networks for biological sequence classification.
method Generalized convolutional kernel networks with recurrent structures.
result Outperforms existing methods for protein classification tasks.
Study on biharmonic hypersurfaces with specific recurrent operators in Euclidean space.
problem Characterizing biharmonic hypersurfaces with recurrent operators.
method Analysis of various recurrent operators and their impact on biharmonic hypersurfaces.
result Some well-known recurrent operators play a significant role in making biharmonic hypersurfaces minimal.
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learningcs.LG
Paper presents RGNN for better graph node representation learning.
problem Node representation learning with graph neural networks.
method Recurrent Graph Neural Network (RGNN) with recurrent units.
result RGNN achieves state-of-the-art results on three benchmarks.
Convolutional LSTM networks outperform GRU in EEG seizure detection.
problem Seizure detection in EEG signals.
method Comparison of LSTM and GRU units, hybrid CNN-RNN architecture, various initialization and regularization methods.
result Convolutional LSTM networks achieve 30% sensitivity at 6 false alarms per 24 hours.
Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networkscs.IR
Paper presents a neural network method for automated bug and ticket classification.
problem Automated classification of bug and ticket content in systems.
method Recurrent neural network with hierarchical attention mechanism.
result The method outperforms previous approaches on two datasets.
Study provides explicit formula for complex 2D Kähler manifold quantization.
problem Quantization of complex 2D locally symmetric Kähler manifolds.
method Deformation quantization with separation of variables, solving recurrence relations.
result Explicit formula for star product on complex 2D locally symmetric Kähler manifolds.
We study the statistical properties of the recurrence intervals between successive trading volumes exceeding a certain threshold . The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2…
Study analyzes stock market dynamics using recurrence measures and transitions.
problem Understanding transitions in stock market dynamics during crises.
method Recurrence plots and networks from nonstationary stock market data.
result Recurrence measures capture transitions in stock market dynamics.
DeepBayes uses neural networks to efficiently estimate parameters in complex dynamical models.
problem Estimating parameters in stochastic, nonlinear dynamical models is challenging.
method DeepBayes leverages deep recurrent neural networks to learn an estimator that minimizes mean-squared error.
result DeepBayes achieves asymptotically equivalent performance to Bayesian estimation methods.
New method prunes recurrent networks efficiently, improving performance.
problem Pruning recurrent neural networks (RNNs) is challenging and often leads to poor performance.
method Data-efficient pruning objective derived from the spectrum of the recurrent Jacobian.
result 95% sparse GRUs significantly improve on existing baselines.
New method interprets recurrent neural networks via excitable network attractors.
problem Understanding the inner workings of recurrent neural networks.
method Excitable network attractors for mechanistic interpretation.
result Recurrent neural networks' behavior can be interpreted using excitable network attractors.
To generalize the notion of recurrent manifold, there are various recurrent like conditions in the literature. In this paper we present a recurrent like structure, namely, \textit{super generalized recurrent manifold}, which generalizes both the hyper generalized recurrent manifold and weakly generalized recurrent mani…
RNN beats Lee-Carter in forecasting mortality rates.
problem Forecasting mortality rates across different demographics.
method Long Short-Term Memory (LSTM) recurrent neural network trained on multiple countries, ages, and sexes.
result RNN model outperforms the Lee-Carter model in mortality rate forecasting.
Solves POMDPs with recurrent neural networks and natural policy gradient.
problem Non-stationarity in optimal policies of POMDPs.
method Integrates recurrent neural networks into natural policy gradient and temporal difference learning.
result Non-asymptotic theoretical guarantees for global optimality up to function approximation.
Simple conformally recurrent spaces are identified as pp-waves.
problem Characterizing conformally recurrent space-times.
method Analyzing dimension n>3 space-times.
result Simple conformally recurrent space-times are conformally recurrent pp-waves.
Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
Deep learning predicts AF recurrence from MRI images without pre-processing.
problem Estimating AF recurrence from MRI images efficiently and accurately.
method Deep learning approach to predict shape descriptors directly from MRI images.
result Deep learning method produces similar outcomes to state-of-the-art methods.
TransformerLSR models longitudinal, recurrent, and survival data jointly.
problem Joint modeling of longitudinal measurements, recurrent events, and survival data with dependencies.
method Transformer-based deep learning framework integrating deep temporal point processes and latent structure representation.
result TransformerLSR effectively models all three components simultaneously, demonstrating necessity and effectiveness through simulations and real-world data.
Using a simple recurrence relation we give a new method to compute Jones polynomials of closed braids: we find a general expansion formula and a rational generating function for Jones polynomials. The method is used to estimate degree of Jones polynomials for some families of braids and to obtain general qualitative re…
Improves sequence modeling with a flow-based recurrent mixture density network.
problem Sequence modeling and sequence-to-sequence mapping applications.
method Generalized recurrent mixture density networks using normalized flow transformations.
result Significantly improved fit to image sequences measured by log-likelihood.
Two special Finsler spaces have been introduced and investigated, namely -recurrent Finsler space and consircularly recurrent Finsler space. The defining properties of these spaces are formulated in terms of the first curvature tensor of Cartan connection. The following three results constitute the main object of …
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plotsq-fin.ST
Study predicts synchronization state of financial time series using cross-recurrence plots.
problem Predicting the state of synchronization of financial time series.
method Cross-correlation analysis and deep learning framework for predicting synchronization state based on cross-recurrence plots.
result Satisfactory performance in predicting synchronization state for certain pairs of stocks.
RNNs are reinterpreted as kernel methods using neural ODEs.
problem Improving generalization and stability of RNNs.
method Connecting RNNs to neural ODEs and reproducing kernel Hilbert spaces.
result RNNs can be viewed as linear functions of a specific feature set.
Gradual training and gradient clipping improve RNN performance.
problem RNNs are hard to train and prone to overfitting.
method Formulated RNN as a Markov chain, gradually trained, and used layer-wise gradient clipping.
result Improvements in language modeling tasks.