Analyzes self-attention in recurrent networks, proving it mitigates vanishing gradients.
problem Vanishing gradients in recurrent networks when capturing long-term dependencies.
method Formal analysis of self-attention's effect on gradient propagation, proposing a relevancy screening mechanism.
result Self-attention mitigates vanishing gradients in recurrent networks, providing guarantees.
Visual Question Answering (VQA) requires AI models to comprehend data in two domains, vision and text. Current state-of-the-art models use learned attention mechanisms to extract relevant information from the input domains to answer a certain question. Thus, robust attention mechanisms are essential for powerful VQA mo…
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.
Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the r…
Music highlights are valuable contents for music services. Most methods focused on low-level signal features. We propose a method for extracting highlights using high-level features from convolutional recurrent attention networks (CRAN). CRAN utilizes convolution and recurrent layers for sequential learning with an att…
Recurrent Neural Network (RNN) has been successfully applied in many sequence learning problems. Such as handwriting recognition, image description, natural language processing and video motion analysis. After years of development, researchers have improved the internal structure of the RNN and introduced many variants…
Improved electrical load forecasting model using Fourier-enhanced RNN.
problem Electrical load time series downscaling with high accuracy and low error.
method Combines recurrent neural network with Fourier seasonal embeddings and self-attention.
result Significantly reduces RMSE across different time horizons compared to existing methods.
Transformer models outperform recurrent ones in modeling hierarchical data.
problem Modeling hierarchical structure in data.
method Introducing Multiresolution Transformer Networks leveraging self-attention.
result Multiresolution Transformer Networks significantly outperform state-of-the-art models on query suggestion datasets.
Self-attentional models improve chatbot efficiency and performance.
problem Training efficient task-oriented dialogue generation systems.
method Applied self-attentional models to three datasets for chatbot training.
result Self-attentional models outperform recurrence-based models in efficiency and performance.
We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established usin…
RIMs improve generalization by specializing modular structures.
problem Improving generalization and robustness to changes in tasks.
method Recurrent Independent Mechanisms (RIMs) architecture with independent dynamics, sparing communication, and selective updates.
result RIMs lead to dramatic improvement in generalization on tasks with varying factors.
The Linear Attention Recurrent Neural Network (LARNN) is a recurrent attention module derived from the Long Short-Term Memory (LSTM) cell and ideas from the consciousness Recurrent Neural Network (RNN). Yes, it LARNNs. The LARNN uses attention on its past cell state values for a limited window size k. The formulas ar…
Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to learn from patterns that are discriminative while suppressing those that are irrelevant for acoustic scene classification. The convolutiona…
The paper proposes a novel model to forecast patent citations using multi-attention recurrent networks.
problem Forecasting forward citations to patents to discover emerging technologies.
method The approach employs a sequence-to-sequence model with an attention-of-attention mechanism to capture dependencies in multiple time sequences.
result The proposed model outperforms state-of-the-art models in forward citation forecasting.
Structured State-Space Duality connects SSMs to masked attention.
problem Connecting SSMs and attention mechanisms for efficient modeling.
method Formalizing and generalizing SSD from scalar-identity to diagonal state matrices.
result Diagonal SSMs match training complexity lower bounds and support richer dynamics.
New insights into how encoder-decoder networks generate attention matrices.
problem Understanding how encoder-decoder networks use attention matrices.
method Decomposing hidden states into temporal and input-driven components.
result Attention matrices are formed based on task requirements, not architecture type.
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.
Paper introduces methods to integrate external knowledge into RNNs using attention mechanisms.
problem Incorporating external knowledge into RNNs for improved performance.
method Proposes three methods: attentional concatenation, feature-based gating, and affine transformation.
result Attentional feature-based gating consistently improves performance across tasks.
Deep learning models predict ICU readmission with varying accuracy.
problem Predicting ICU readmission risk using deep learning architectures.
method Several deep learning architectures including attention-based models, recurrent layers, neural ODEs, and embeddings were trained on MIMIC-III data.
result Attention-based models with neural ODEs achieved highest predictive accuracy.
RAM extends attention-based mechanism for existence determination.
problem Binary determination of object existence in cluttered images.
method Recurrent attention model (RAM) with k-maximum aggregation layer and new reward mechanism. result Significant efficiency and accuracy improvement over existing approaches.
Study improves stock price prediction using advanced ML models.
problem Improving financial forecasting accuracy in stock markets.
method Evaluation of RNN architectures including LSTM, GRU, and attention-based models.
result Attention-based models outperform others in capturing complex dependencies.
Neural network predicts cardiovascular events from EHRs with high accuracy.
problem Predicting onset of cardiovascular diseases from electronic health records.
method Multi-task gated recurrent units with attention mechanism.
result Model outperforms clinical risk scores in predicting stroke and myocardial infarction.
New approach improves linear-time attention for language models.
problem Challenges of quadratic attention in long-sequence modelling, especially for discrete data.
method Reinterpreting linear attention through latent probabilistic graphical models, introducing asymmetric structure and recurrent parameterisation.
result Our model achieves competitive performance and outperforms existing linear attention variants on language modelling benchmarks.
Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent …
New approach to understand recurrent policies as FSMs without minimization.
problem Minimization of FSMs obscures the semantics of policy decisions.
method Start with unminimized FSM, apply interpretable reductions, use attention tool.
result Reveals insights into policy decisions not previously noticed.
Hybrid model predicts flow and pressure in water systems.
problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.
Improved speech enhancement with MNTFA using time-frequency attention.
problem Speech enhancement with limited model size and memory.
method Designing MNTFA with self-attention modules for long sequences and joint training.
result MNTFA achieves better performance with fewer parameters than DPCRN.
Improved stock price prediction using attention modules and news sentiment.
problem Predicting stock prices with non-stationary and non-parametric data.
method α_{t}-RIM architecture with attention modules and exponentially smoothed recurrent neural network.
result The αt-RIM outperforms state-of-the-art models in predicting unseen data. Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Study uses attention-based method to detect different types of online harassment.
problem Detecting different types of online harassment in social media content.
method Multi-attention based approach using Recurrent Neural Networks to address imbalanced data.
result Demonstrates effectiveness of attention-based mechanism for detecting various types of online harassment.
DAUM networks improve weather forecasting with attention and multistream features.
problem Improving weather forecasting accuracy using deep learning.
method Proposes DAUM networks combining tensorial unistream and multistream representations with attention mechanisms.
result Adding self-attention blocks enhances forecasting performance.
Self-attention improves satellite time series classification without preprocessing.
problem Efficiently classifying raw satellite time series data.
method Comparison of deep learning models including self-attention, 1D-convolutions, recurrence, and random forest.
result Self-attention and recurrent neural networks outperform convolutional neural networks on raw satellite time series.
Efficient graph generation with GRAN using attention and sampling.
problem Generating high-quality graphs efficiently.
method Graph Recurrent Attention Networks (GRAN) with attention mechanisms and sampling.
result State-of-the-art time efficiency and sample quality on benchmarks.
We explore solutions for automated labeling of content in bug trackers and customer support systems. In order to do that, we classify content in terms of several criteria, such as priority or product area. In the first part of the paper, we provide an overview of existing methods used for text classification. These met…
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.
FMA-ETA predicts travel time using FFN with attention.
problem Estimating travel time from spatial-temporal data.
method FFN with Multi-factor self-attention mechanism.
result FMA-ETA outperforms state-of-the-art methods in prediction accuracy with faster inference.
A new model uses attention and Gaussian processes for efficient time-series generation.
problem Computational inefficiency and uncertainty underestimation in sequence transduction.
method Attention-based Gaussian process network for real-valued sequence generation.
result The model improves training efficiency and learns factorized generative distribution.
In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To th…
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.
Novel method segments heart sound signals using LSTMs with attention.
problem Heart sound segmentation for diagnostic applications.
method Bidirectional LSTMs with attention mechanisms.
result State-of-the-art performance on multiple benchmarks.
Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict. Our study utilized regression models and attention-based recurre…
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them ca…
This paper improves image super-resolution by integrating cross-scale non-local attention.
problem Improving image super-resolution by leveraging long-range and cross-scale feature correlations.
method Proposes a Cross-Scale Non-Local (CS-NL) attention module integrated into a recurrent neural network.
result Significantly improved performance on SISR benchmarks.
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.
CLVSA predicts financial market trends using LSTM and attention mechanisms.
problem Predicting trends in financial markets due to complex interactions.
method Hybrid model combining LSTM, sequence-to-sequence, attention, and convolutional LSTM.
result CLVSA outperforms basic models in predicting financial market trends.
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
Analyzes attention structure in GPT-2 model, revealing specific patterns.
problem Understanding attention mechanisms in Transformer models.
method Visualized and analyzed attention for GPT-2 model, examining interactions over a corpus.
result Attention targets different parts of speech at varying depths, aligning with dependency relations in middle layers.
Regularized recurrent attention filter combines sensor inputs.
problem Combining information from different sensor modalities.
method Regularized recurrent attention filter, co-learning mechanism, probabilistic graphical model.
result Dynamic sensor fusion and latent representation recovery.