RANP improves neural processes for sequential data.
problem Capturing temporal order and recurrent structure from sequential data.
method Incorporated ANP into a recurrent neural network.
result RANP outperforms NPs and LSTMs in 1D regression and autonomous-driving tasks.
Recurrent Neural Processes model time series with conditional independence to capture slow variabilities efficiently.
problem Modeling time series data with slow long-term variabilities efficiently.
method Recurrent Neural Processes (RNP) model state space with conditional independence among subsequences.
result RNP state spaces improve predictive performance on real-world time-series data and nonlinear system identification.
RecNets use RNNs to process image channels in a compact, recurrent way.
problem Creating efficient neural network architectures for computer vision.
method Introducing RecNets with CRC layers that simulate recurrent processing of image channels.
result RecNets achieve superior size-accuracy trade-off compared to other compact models.
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.
Next-gen reservoir computers fail to predict complex processes, highlighting need for better architectures.
problem Predicting complex, non-Markovian processes with recurrent neural networks.
method Lower bound from Fano's inequality and analysis of large probabilistic state machines.
result Next-generation reservoir computers have an error probability at least 60% higher than optimal for highly non-Markovian processes.
Process Mining consists of techniques where logs created by operative systems are transformed into process models. In process mining tools it is often desired to be able to classify ongoing process instances, e.g., to predict how long the process will still require to complete, or to classify process instances to diffe…
Bayesian Recurrent Neural Networks improve fault detection and identification in manufacturing.
problem Detect and identify faults in chemical processes to ensure optimal operations.
method Bayesian Recurrent Neural Networks (BRNNs) with variational dropout.
result BRNNs provide uncertainty estimates for fault detection and identification.
This study analyzes how RNNs process context in sentiment analysis.
problem Understanding how recurrent neural networks process context in sentiment analysis.
method Developed methods to reverse engineer RNNs, identifying contextual effects and quantifying their strength and timescale.
result Identified inputs that induce contextual effects and quantified their properties.
Recurrent Networks are one of the most powerful and promising artificial neural network algorithms to processing the sequential data such as natural languages, sound, time series data. Unlike traditional feed-forward network, Recurrent Network has a inherent feed back loop that allows to store the temporal context info…
New method compresses neural networks up to 14x with minimal performance loss.
problem Compressing neural networks for real-time applications.
method Post-training rank-selection method called Rank-Tuning.
result High compression rates with minimal performance degradation.
UNIPoint universally approximates point process intensities.
problem How to precisely describe the flexibility of point process models.
method Proof using Stone-Weierstrass Theorem, transfer functions, and recurrent neural networks.
result UNIPoint performs better than other models on synthetic and real-world datasets.
Traditional Recurrent Neural Networks assume vectorized data as inputs. However many data from modern science and technology come in certain structures such as tensorial time series data. To apply the recurrent neural networks for this type of data, a vectorisation process is necessary, while such a vectorisation leads…
Paper establishes bounds for RNN-TPPs, showing four-layer networks can achieve vanishing errors.
problem Understanding theoretical limits of RNN-TPPs.
method Characterized RNN complexity, constructed neural approximations, applied truncation technique.
result Four-layer RNN-TPPs can achieve vanishing generalization errors.
Model captures complex neural dynamics with Gaussian process and RNN.
problem Challenges in extracting latent dynamics from noisy neural data.
method Gaussian process recurrent neural networks (GP-RNN) for nonlinear, non-Markovian dynamics.
result Outperforms state-of-the-art methods in reconstructing latent dynamics.
In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large nu…
Dual neural network architecture improves accuracy and interpretability.
problem Improving neural network interpretability and accuracy.
method Stacked recurrent and feedforward layers, binary activation function.
result Binary activation leads to simpler, more interpretable models with higher accuracy.
This paper improves speech synthesis using a DGP with SRU for naturalness.
problem Improving naturalness in synthetic speech.
method Deep Gaussian process with a recurrent architecture using SRU.
result SRU-DGP outperforms other models in naturalness of synthetic speech.
New algorithm speeds up RNN time series prediction by filtering noise.
problem Predicting smooth trajectories from noisy time series data.
method Analyzed RNN dynamics to propose an efficient noise filtering algorithm.
result Significant speedup in predictive process without accuracy loss.
Paper proposes a faster RAE with sequence-aware encoding.
problem Training recurrent autoencoders is challenging and time-consuming.
method Introduces a recurrent autoencoder with sequence-aware encoding using 1D convolutional layers.
result The proposed autoencoder trains faster than standard RAE.
New attention mechanism improves meta-transfer learning in dynamic tasks.
problem Underfitting in meta-transfer learning with dynamic tasks.
method Proposed Recurrent Memory Reconstruction (RMR) attention mechanism.
result ASNP-RMR significantly outperforms baselines in various tasks.
Proposes a neural network solution for queuing dynamics.
problem Modeling service system dynamics with limited parametric assumptions.
method Combines recurrent neural networks and generative adversarial networks.
result Evaluates solution on various datasets, demonstrating effectiveness.
Generative model uses recurrent neural networks to predict graph edges.
problem Graph generation with Machine Learning is an open problem.
method Sequential graph generation with two recurrent neural networks.
result Approach generates unique graphs with similar structural properties.
The paper predicts workload using process mining and neural networks.
problem Predicting workload in business processes.
method Process mining logs, combined with recurrent neural networks.
result Workload prediction achieved with an MAPE score of 19%.
Proposes a new model for time-to-event prediction with uncertainty quantification.
problem Lack of uncertainty in time-to-event predictions using recurrent neural networks.
method Deep Kernel Accelerated Failure Time models combining RNN and sparse Gaussian Process.
result Model delivers better uncertainty estimates compared to related methods.
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complement…
New kernel-based models improve on traditional neural methods in sequence modeling.
problem Sequence modeling challenges in natural language processing and neuroscience.
method Kernel-based recurrent neural networks and convolutional neural networks.
result Kernel-based models perform on par or better than traditional neural methods.
Paper uses LSTM to predict inflation, finds it performs well over long periods.
problem Predicting inflation using machine learning models.
method Applies LSTM, a recurrent neural network, to forecast inflation over time.
result LSTM model performs well at long horizons and during uncertain economic times.
NeuroMemFPP uses LSTM to estimate FPP parameters with high accuracy.
problem Estimating parameters of fractional Poisson process with memory and long-range dependence.
method Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for parameter estimation.
result The LSTM-based approach reduces MSE by about 55.3% compared to traditional MOM method.
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research direction…
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems. GCRNNs use convolutional filter banks to keep th…
A new memory-efficient sign language translation model reduces weight usage.
problem Memory constraints in real-time sign language translation.
method Variational Bayesian sequence-to-sequence network with Gaussian posterior and Indian Buffet Process prior.
result The proposed model achieves substantial weight compression without compromising performance.
Adaptive lateral connections improve visual action recognition.
problem Feedforward neural models lack feedback and lateral connections like the primate visual cortex.
method Dynamic weights in recurrent lateral connections, iteratively reintroduced input.
result Significant performance gains in visual action recognition without pretraining.
In this work we compare different batch construction methods for mini-batch training of recurrent neural networks. While popular implementations like TensorFlow and MXNet suggest a bucketing approach to improve the parallelization capabilities of the recurrent training process, we propose a simple ordering strategy tha…
ARMIN improves memory efficiency and lightness in neural networks.
problem Complex memory addressing and inefficient memory information exploitation in previous MANNs.
method ARMIN uses hidden state for automatic memory addressing and a novel RNN cell for memory integration.
result ARMIN achieves lower computational overhead and similar performances compared to vanilla LSTM.
Study evaluates CL methods in RNNs, highlighting differences from feedforward networks.
problem Preventing catastrophic forgetting in RNNs processing sequential data.
method Comprehensive evaluation of CL methods, including elastic weight consolidation and hypernetworks.
result Weight-importance methods perform similarly regardless of sequence length but require more stability for high working memory demands.
A new approach to learning in brain-like networks using adversarial algorithms.
problem Complex inter-dependencies in brain-like networks not compatible with conditional independence assumptions.
method Adversarial algorithm for learning models of perceptual processing.
result The approach can mimic known neural phenomena and yields testable hypotheses.
Paper compares GRU and LSTM for predicting wildfire spread direction.
problem Predicting wildfire spread direction with limited data.
method Comparison of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks.
result GRU performs better for longer time series than LSTM.
Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural networks (uRNNs), which use unitary recurrence matrices, have recently been proposed as a means to avoid these issues. However, in previous …
Time-aware neural models improve system identification from unevenly sampled data.
problem Improving system identification from continuous variables with unevenly sampled time data.
method Introduced a time-aware and stationary extension of recurrent neural networks.
result Demonstrated improved performance on industrial input/output processes.
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.
Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.
problem Capturing non-stationary dependencies in point process data.
method Approximates the influence kernel with a novel low-rank decomposition and introduces a log-barrier penalty to maintain non-negativity.
result Demonstrates superior performance and computational efficiency compared to state-of-the-art methods.
Nonlinear RNNs' memory capacity varies widely, making it impractical.
problem The usefulness of memory capacity as a metric for linear RNNs is questioned.
method Analysis of random nonlinear RNNs with varying input scales.
result Memory capacity of nonlinear RNNs is arbitrary and impractical.
Improved speech recognition for voice assistants by analyzing speech data.
problem Reducing false triggers in speech-enabled assistants.
method Post-processing LVCSR hypothesis lattice with a Bidirectional Lattice Recurrent Neural Network (LatticeRNN).
result LatticeRNN significantly improves detection accuracy over traditional methods.
Study improves forecasting in betting markets using novel neural networks.
problem Improving short-term price movement predictions in betting exchanges.
method Innovative convolutional attention mechanisms applied to recurrent neural networks and bi-dimensional layers.
result All proposed innovations positively impact classification task performance.
Recurrent networks learn beliefs from history in partially observable environments.
problem Learning optimal policies in partially observable environments.
method Trained recurrent neural networks to approximate value functions, measuring mutual information between hidden states and beliefs.
result Recurrent networks' hidden states correlate with beliefs of relevant state variables, improving expected return.
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and…
Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which i…
Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many sequence-to-sequence modeling tasks. However, RNNs are difficult to train and tend to suffer from overfitting. Motivated by the Data Processing Inequality (DPI), we formulate the multi-layered network as a Markov chain, introducing a training met…