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Future Frontiers in Deep Learning: From Static to Dynamic Neural Network Technologies

https://doi.org/10.2139/ssrn.4832892
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inferredcites →https://doi.org/10.1007/978-3-030-01219-9_11openalex-extracted
Multimodal Unsupervised Image-to-Image Translation
inferredcites →https://doi.org/10.1007/978-3-030-01240-3_16openalex-extracted
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
inferredcites →https://doi.org/10.1007/978-3-030-01240-3_4openalex-extracted
Learning to Zoom: A Saliency-Based Sampling Layer for Neural Networks
inferredcites →https://doi.org/10.1007/978-3-030-01246-5_1openalex-extracted
Convolutional Networks with Adaptive Inference Graphs
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_8openalex-extracted
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
inferredcites →https://doi.org/10.1007/978-3-030-28954-6_10openalex-extracted
Layer-Wise Relevance Propagation: An Overview
inferredcites →https://doi.org/10.1007/978-3-030-58452-8_31openalex-extracted
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
inferredcites →https://doi.org/10.1007/978-3-030-58571-6_6openalex-extracted
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
inferredcites →https://doi.org/10.1007/978-3-030-58577-8_11openalex-extracted
Spatial-Adaptive Network for Single Image Denoising
inferredcites →https://doi.org/10.1007/978-3-319-44781-0_8openalex-extracted
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_39openalex-extracted
Deep Networks with Stochastic Depth
inferredcites →https://doi.org/10.1007/bf00344251openalex-extracted
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
inferredcites →https://doi.org/10.1007/bf00992696openalex-extracted
Simple statistical gradient-following algorithms for connectionist reinforcement learning
inferredcites →https://doi.org/10.1007/s10489-022-03383-wopenalex-extracted
Dynamic channel pruning via activation gates
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106960openalex-extracted
BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation
inferredcites →https://doi.org/10.1016/j.eij.2020.02.007openalex-extracted
A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks
inferredcites →https://doi.org/10.1016/j.ijin.2022.11.005openalex-extracted
An inception V3 approach for malware classification using machine learning and transfer learning
inferredcites →https://doi.org/10.1016/j.neucom.2019.08.018openalex-extracted
SCAR: Spatial-/channel-wise attention regression networks for crowd counting
inferredcites →https://doi.org/10.1016/j.neucom.2021.07.045openalex-extracted
Pruning and quantization for deep neural network acceleration: A survey
inferredcites →https://doi.org/10.1016/j.neunet.2023.02.040openalex-extracted
A general framework for robust stability analysis of neural networks with discrete time delays
inferredcites →https://doi.org/10.1016/j.patrec.2023.08.001openalex-extracted
The effectiveness of T5, GPT-2, and BERT on text-to-image generation task
inferredcites →https://doi.org/10.1109/34.655647openalex-extracted
Neural network-based face detection
inferredcites →https://doi.org/10.1109/78.650093openalex-extracted
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/access.2020.2982433openalex-extracted
Water Level Prediction Model Based on GRU and CNN
inferredcites →https://doi.org/10.1109/biorob60516.2024.10719798openalex-extracted
Sequential Image Classification of Human-Robot Walking Environments Using Temporal Neural Networks
inferredcites →https://doi.org/10.1109/ccdc.2019.8832535openalex-extracted
Facial Expression Recognition Based on Improved LeNet-5 CNN
inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-extracted
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2013.446openalex-extracted
Deep Convolutional Network Cascade for Facial Point Detection
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-extracted
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7299170openalex-extracted
A convolutional neural network cascade for face detection
inferredcites →https://doi.org/10.1109/cvpr.2016.234openalex-extracted
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
inferredcites →https://doi.org/10.1109/cvpr.2016.293openalex-extracted
End-to-End Learning of Action Detection from Frame Glimpses in Videos
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-extracted
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.194openalex-extracted
Spatially Adaptive Computation Time for Residual Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.205openalex-extracted
More is Less: A More Complicated Network with Less Inference Complexity
inferredcites →https://doi.org/10.1109/cvpr.2017.207openalex-extracted
Scale-Aware Face Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-extracted
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-extracted
Aggregated Residual Transformations for Deep Neural Networks
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Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
inferredcites →https://doi.org/10.1109/cvpr.2018.00291openalex-extracted
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions
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Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
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SBNet: Sparse Blocks Network for Fast Inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00919openalex-extracted
BlockDrop: Dynamic Inference Paths in Residual Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00188openalex-extracted
Adaptively Connected Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00244openalex-extracted
Semantic Image Synthesis With Spatially-Adaptive Normalization
inferredcites →https://doi.org/10.1109/cvpr.2019.00953openalex-extracted
Deformable ConvNets V2: More Deformable, Better Results
inferredcites →https://doi.org/10.1109/cvpr.2019.01147openalex-extracted
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization
inferredcites →https://doi.org/10.1109/cvpr.2019.01288openalex-extracted
Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00239openalex-extracted
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00244openalex-extracted
Resolution Adaptive Networks for Efficient Inference
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00341openalex-extracted
Anisotropic Convolutional Networks for 3D Semantic Scene Completion
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00858openalex-extracted
Learning Dynamic Routing for Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00982openalex-extracted
PointRend: Image Segmentation As Rendering
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00797openalex-extracted
Dynamic Region-Aware Convolution
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00203openalex-extracted
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
inferredcites →https://doi.org/10.1109/icassp.2013.6639344openalex-extracted
New types of deep neural network learning for speech recognition and related applications: an overview
inferredcites →https://doi.org/10.1109/icassp39728.2021.9414505openalex-extracted
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.67openalex-extracted
Conditioned Regression Models for Non-blind Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/iccv.2017.155openalex-extracted
Channel Pruning for Accelerating Very Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2017.298openalex-extracted
Learning Efficient Convolutional Networks through Network Slimming
inferredcites →https://doi.org/10.1109/iccv.2017.379openalex-extracted
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
inferredcites →https://doi.org/10.1109/iccv.2019.00629openalex-extracted
DynamoNet: Dynamic Action and Motion Network
inferredcites →https://doi.org/10.1109/iccv.2019.00651openalex-extracted
KPConv: Flexible and Deformable Convolution for Point Clouds
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-extracted
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/icpr48806.2021.9413153openalex-extracted
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos
inferredcites →https://doi.org/10.1109/mdm48529.2020.00070openalex-extracted
DACNN: Dynamic Weighted Attention with Multi-channel Convolutional Neural Network for Emotion Recognition
inferredcites →https://doi.org/10.1109/tip.2020.3018269openalex-extracted
Learning Layer-Skippable Inference Network
inferredcites →https://doi.org/10.1109/tnnls.2011.2178563openalex-extracted
Stability and Dissipativity Analysis of Static Neural Networks With Time Delay
inferredcites →https://doi.org/10.1109/tnnls.2020.2979517openalex-extracted
Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks
inferredcites →https://doi.org/10.1109/tnnls.2022.3161284openalex-extracted
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
inferredcites →https://doi.org/10.1109/tpami.2019.2959322openalex-extracted
Anytime Recognition with Routing Convolutional Networks
inferredcites →https://doi.org/10.1109/tpami.2021.3117837openalex-extracted
Dynamic Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tpami.2023.3263826openalex-extracted
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/wacv.2019.00114openalex-extracted
Pixel-Wise Attentional Gating for Scene Parsing
inferredcites →https://doi.org/10.1109/wacv56688.2023.00027openalex-extracted
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editings
inferredcites →https://doi.org/10.1115/gt2006-90236openalex-extracted
Static and Dynamic Neural Networks for Simulation and Optimization of Cogeneration Systems
inferredcites →https://doi.org/10.1137/1.9781611976700.56openalex-extracted
Inter-Series Attention Model for COVID-19 Forecasting
inferredcites →https://doi.org/10.1145/3065386openalex-extracted
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3292500.3330662openalex-extracted
Multi-Horizon Time Series Forecasting with Temporal Attention Learning
inferredcites →https://doi.org/10.1145/3340531.3411973openalex-extracted
EPNet: Learning to Exit with Flexible Multi-Branch Network
inferredcites →https://doi.org/10.1145/3343031.3350961openalex-extracted
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
inferredcites →https://doi.org/10.1145/3362743.3362965openalex-extracted
Skipping RNN State Updates without Retraining the Original Model
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.593openalex-extracted
The Right Tool for the Job: Matching Model and Instance Complexities
inferredcites →https://doi.org/10.18653/v1/2023.findings-acl.737openalex-extracted
Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning
inferredcites →https://doi.org/10.25791/pribor.8.2023.1430openalex-extracted
Convolutional Neural Networks for Image Classification
inferredcites →https://doi.org/10.3390/agriculture12081083openalex-extracted
Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network
inferredcites →https://doi.org/10.3390/agriculture12122047openalex-extracted
DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification
inferredcites →https://doi.org/10.3390/diagnostics13030385openalex-extracted
Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions
inferredcites →https://doi.org/10.3390/s21082852openalex-extracted
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
inferredcites →https://doi.org/10.3390/s22145446openalex-extracted
An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network
inferredcites →https://doi.org/10.3850/9783981537079_0819openalex-extracted
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
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Recurrent Models of Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-extracted
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.7755openalex-extracted
Multiple Object Recognition with Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1602.02830openalex-extracted
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
inferredcites →https://doi.org/10.48550/arxiv.1603.08199openalex-extracted
Recurrent Mixture Density Network for Spatiotemporal Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1603.08983openalex-extracted
Adaptive Computation Time for Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1608.04493openalex-extracted
Dynamic Network Surgery for Efficient DNNs
inferredcites →https://doi.org/10.48550/arxiv.1608.08710openalex-extracted
Pruning Filters for Efficient ConvNets
inferredcites →https://doi.org/10.48550/arxiv.1609.01704openalex-extracted
Hierarchical Multiscale Recurrent Neural Networks
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Categorical Reparameterization with Gumbel-Softmax
inferredcites →https://doi.org/10.48550/arxiv.1611.06188openalex-extracted
Variable Computation in Recurrent Neural Networks
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
inferredcites →https://doi.org/10.48550/arxiv.1702.07811openalex-extracted
Adaptive Neural Networks for Efficient Inference
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Multi-Scale Dense Networks for Resource Efficient Image Classification
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1708.06834openalex-extracted
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1711.02085openalex-extracted
Neural Speed Reading via Skim-RNN
inferredcites →https://doi.org/10.48550/arxiv.1804.04732openalex-extracted
Multimodal Unsupervised Image-to-Image Translation
inferredcites →https://doi.org/10.48550/arxiv.1805.11394openalex-extracted
A novel channel pruning method for deep neural network compression
inferredcites →https://doi.org/10.48550/arxiv.1806.04342openalex-extracted
Focused Hierarchical RNNs for Conditional Sequence Processing
inferredcites →https://doi.org/10.48550/arxiv.1810.05331openalex-extracted
Dynamic Channel Pruning: Feature Boosting and Suppression
inferredcites →https://doi.org/10.48550/arxiv.1904.00761openalex-extracted
Neural Speed Reading with Structural-Jump-LSTM
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Dynamic Neural Network Channel Execution for Efficient Training
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1907.06627openalex-extracted
Batch-Shaping for Learning Conditional Channel Gated Networks
inferredcites →https://doi.org/10.48550/arxiv.1910.06809openalex-extracted
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
inferredcites →https://doi.org/10.48550/arxiv.2005.10627openalex-extracted
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
inferredcites →https://doi.org/10.48550/arxiv.2006.04152openalex-extracted
BERT Loses Patience: Fast and Robust Inference with Early Exit
inferredcites →https://doi.org/10.48550/arxiv.2010.05300openalex-extracted
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
inferredcites →https://doi.org/10.48550/arxiv.2011.05005openalex-extracted
Deep Multimodal Fusion by Channel Exchanging
inferredcites →https://doi.org/10.48550/arxiv.2101.03961openalex-extracted
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
inferredcites →https://doi.org/10.48550/arxiv.2104.05085openalex-extracted
Group Convolutional Neural Networks Improve Quantum State Accuracy
inferredcites →https://doi.org/10.48550/arxiv.2108.10394openalex-extracted
Dynamic Network Quantization for Efficient Video Inference
inferredcites →https://doi.org/10.48550/arxiv.2202.13560openalex-extracted
ConvNeXt-backbone HoVerNet for nuclei segmentation and classification
inferredcites →https://doi.org/10.48550/arxiv.2402.04882openalex-extracted
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
inferredcites →https://doi.org/10.5244/c.29.32openalex-extracted
Real-Time Pedestrian Detection with Deep Network Cascades
inferredcites →https://doi.org/10.5555/1639537.1639542openalex-extracted
Multilayer perceptron and neural networks
inferredcites →https://doi.org/10.65215/2q58a426openalex-extracted
Attention Is All You Need
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.5281/zenodo.17754506openalex-snapshot
Abstract Functional Language Logic: A Competitive Mixture of Experts Architecture for Paradox-Free Reasoning and Adaptive Intelligence
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