Papers Sequential Image Classification
“Sequential Image Classification” 태그가 달린 논문 44편 · 필터 해제
Minion Gated Recurrent Unit for Continual Learning
The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge …
Continual Learningimage-classificationImage ClassificationSequential Image ClassificationLearning Long Sequences in Spiking Neural Networks
Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on modern sequential tasks…
image-classificationImage ClassificationSequential Image ClassificationState Space ModelsDelayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications. Nevertheless, vanilla RNNs are conf…
Computational EfficiencyGesture Recognitionimage-classificationImage Classification+3Traveling Waves Encode the Recent Past and Enhance Sequence Learning
Traveling waves of neural activity have been observed throughout the brain at a diversity of regions and scales; however, their precise computational role is still debated. One physically inspired hypothesis suggests tha…
image-classificationImage ClassificationSequential Image ClassificationSequence Modeling with Multiresolution Convolutional Memory
Efficiently capturing the long-range patterns in sequential data sources salient to a given task -- such as classification and generative modeling -- poses a fundamental challenge. Popular approaches in the space tradeof…
Density EstimationListOpsSequential Image ClassificationSMPConv: Self-moving Point Representations for Continuous Convolution
Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels ha…
DescriptiveImage ClassificationSequential Image ClassificationTime Series AnalysisResurrecting Recurrent Neural Networks for Long Sequences
Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train. Deep state-space models (SSMs) have recently been shown to perform remarkably well on long sequence mode…
ClassificationComputational EfficiencySequential Image ClassificationState Space ModelsVCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory
Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data. However, there are many cases where …
image-classificationImage ClassificationSequential Image Classificationtext-classification+1Image Classification using Sequence of Pixels
This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term m…
Classificationimage-classificationImage ClassificationSequential Image ClassificationEfficient recurrent architectures through activity sparsity and sparse back-propagation through time
Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap b…
Gesture RecognitionLanguage ModelingLanguage ModellingSequential Image Classification+1Efficiently Modeling Long Sequences with Structured State Spaces
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models incl…
Data AugmentationLanguage ModelingLanguage ModellingListOps+3Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling po…
Computational Efficiencyimage-classificationImage ClassificationMemorization+3FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kernel sizes at different layers, but explor…
DescriptiveImage ClassificationSequential Image ClassificationTime Series AnalysisLong Expressive Memory for Sequence Modeling
We propose a novel method called Long Expressive Memory (LEM) for learning long-term sequential dependencies. LEM is gradient-based, it can efficiently process sequential tasks with very long-term dependencies, and it is…
Language ModelingLanguage ModellingSequential Image Classificationspeech-recognition+4RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in…
Sequential Image ClassificationCombining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling po…
Computational Efficiencyimage-classificationImage ClassificationMemorization+3UnICORNN: A recurrent model for learning very long time dependencies
The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem. To overcome this, we prop…
Sentiment AnalysisSequential Image ClassificationTime Series AnalysisTime Series ClassificationSequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these …
Autonomous DrivingAutonomous NavigationAutonomous VehiclesImage Retrieval+13Parallelizing Legendre Memory Unit Training
Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant…
Machine TranslationSentiment AnalysisSequential Image ClassificationTranslationCKConv: Continuous Kernel Convolution For Sequential Data
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Co…
Permuted-MNISTSequential Image ClassificationTime Series Analysis