Papers Sequential Image Classification
“Sequential Image Classification” 태그가 달린 논문 44편 · 필터 해제
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex …
Autonomous DrivingAutonomous NavigationAutonomous VehiclesImage Retrieval+12Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gra…
Sentiment AnalysisSequential Image ClassificationHiPPO: Recurrent Memory with Optimal Polynomial Projections
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the online compression of continuous…
Permuted-MNISTSequential Image ClassificationTime SeriesTime Series AnalysisLearning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and …
image-classificationImage ClassificationLanguage ModelingLanguage Modelling+3Lipschitz Recurrent Neural Networks
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz n…
Language ModelingLanguage ModellingSequential Image ClassificationLearning Long-Term Dependencies in Irregularly-Sampled Time Series
Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series. These models, however, face difficulties when the input data possess long-term dependenc…
Sequential Image ClassificationTime SeriesTime Series AnalysisRecurrent Highway Networks with Grouped Auxiliary Memory
Recurrent neural networks (RNNs) are challenging to train, let alone those with deep spatial structures. Architectures built upon highway connections such as Recurrent Highway Network (RHN) were developed to allow larger…
image-classificationImage ClassificationLanguage ModelingLanguage Modelling+2Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to …
Sequential Image ClassificationTime SeriesTime Series AnalysisTime Series PredictionGating Revisited: Deep Multi-layer RNNs That Can Be Trained
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients. Stacking r…
Action RecognitionAction Recognition In VideosLanguage ModellingMusic Modeling+1Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time. However, their saturation property introduces problems of its own. For example, i…
image-classificationImage ClassificationLanguage ModelingLanguage Modelling+3Deep Independently Recurrent Neural Network (IndRNN)
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks. To address these proble…
Language ModellingSequential Image ClassificationSkeleton Based Action RecognitionR-Transformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequenti…
Language ModellingMusic ModelingPositionSequential Image ClassificationAntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Recurrent neural networks have gained widespread use in modeling sequential data. Learning long-term dependencies using these models remains difficult though, due to exploding or vanishing gradients. In this paper, we dr…
Sequential Image ClassificationLearning to Remember More with Less Memorization
Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, …
MemorizationSentiment AnalysisSequential Image ClassificationText ClassificationTrellis Networks for Sequence Modeling
We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct …
Language ModelingLanguage ModellingSequential Image ClassificationLong short-term memory and learning-to-learn in networks of spiking neurons
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain. But computing and learning capabilities of RSNN models have remained poor, at least in comparison wi…
Reinforcement LearningSequential Image ClassificationSpeech RecognitionIndependently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-…
Language ModellingSequential Image ClassificationSkeleton Based Action RecognitionAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthes…
Audio SynthesisLanguage ModellingMachine TranslationMusic Modeling+3Cortical microcircuits as gated-recurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common computational principles. However, such pr…
image-classificationImage ClassificationLanguage ModellingSequential Image ClassificationDilated Recurrent Neural Networks
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallel…
Sequential Image Classification