| MAPLE: Microprocessor A Priori for Latency Estimation | 0 | 0.34 | 2022 |
| MAPLE-Edge: A Runtime Latency Predictor for Edge Devices | 0 | 0.34 | 2022 |
| CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection | 0 | 0.34 | 2022 |
| Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement | 0 | 0.34 | 2021 |
| Outliernets: Highly Compact Deep Autoencoder Network Architectures For On-Device Acoustic Anomaly Detection | 0 | 0.34 | 2021 |
| Real-Time Vehicle Make and Model Recognition Using Unsupervised Feature Learning | 0 | 0.34 | 2020 |
| Learn2perturb: An End-To-End Feature Perturbation Learning To Improve Adversarial Robustness | 0 | 0.34 | 2020 |
| SANE - Exploring Adversarial Robustness With Stochastically Activated Network Ensembles. | 0 | 0.34 | 2019 |
| A Random Field Computational Adaptive Optics Framework For Optical Coherence Microscopy | 0 | 0.34 | 2019 |
| Dynamic Representations Toward Efficient Inference on Deep Neural Networks by Decision Gates | 0 | 0.34 | 2019 |
| SANE - Towards Improved Prediction Robustness via Stochastically Activated Network Ensembles. | 0 | 0.34 | 2019 |
| muNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification. | 0 | 0.34 | 2018 |
| Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection | 8 | 0.45 | 2018 |
| Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks. | 7 | 0.49 | 2018 |
| MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection. | 0 | 0.34 | 2018 |
| Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition. | 0 | 0.34 | 2018 |
| StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks. | 0 | 0.34 | 2018 |
| Efficient Inference on Deep Neural Networks by Dynamic Representations and Decision Gates. | 0 | 0.34 | 2018 |
| Real-Time Embedded Motion Detection via Neural Response Mixture Modeling. | 4 | 0.37 | 2018 |
| FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis. | 3 | 0.43 | 2018 |
| Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions. | 0 | 0.34 | 2017 |
| Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks. | 5 | 0.44 | 2017 |
| Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection. | 0 | 0.34 | 2017 |
| Discovery Radiomics Via A Mixture Of Deep Convnet Sequencers For Multi-Parametric Mri Prostate Cancer Classification | 1 | 0.35 | 2017 |
| Compensated Row-Column Ultrasound Imaging System Using Three Dimensional Random Fields | 0 | 0.34 | 2017 |
| Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video. | 12 | 0.55 | 2017 |
| Discovery Radiomics For Pathologically-Proven Computed Tomography Lung Cancer Prediction | 2 | 0.42 | 2017 |
| Deep Randomly-Connected Conditional Random Fields For Image Segmentation. | 2 | 0.36 | 2017 |
| Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection. | 0 | 0.34 | 2017 |
| Deeppredict: A Deep Predictive Intelligence Platform For Patient Monitoring | 1 | 0.40 | 2017 |
| Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG). | 0 | 0.34 | 2016 |
| NeRD: a Neural Response Divergence Approach to Visual Salience Detection. | 0 | 0.34 | 2016 |
| Sparse Reconstruction of Compressive Sensing Multi-Spectral Data Using an Inter-Spectral Multi-Layered Conditional Random Field Model. | 1 | 0.35 | 2016 |
| EvoNet: Evolutionary Synthesis of Deep Neural Networks. | 0 | 0.34 | 2016 |
| Image Restoration via Deep-Structured Stochastically Fully-Connected Conditional Random Fields (DSFCRFs) for Very Low-Light Conditions | 0 | 0.34 | 2016 |
| Fully Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-Spot Detection In SAR Imagery. | 0 | 0.34 | 2016 |
| Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding. | 6 | 0.55 | 2016 |
| Embedded Motion Detection via Neural Response Mixture Background Modeling. | 2 | 0.36 | 2016 |
| Random feature maps via a Layered Random Projection (LARP) framework for object classification | 0 | 0.34 | 2016 |
| Real-Time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG). | 1 | 0.37 | 2016 |
| Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields | 0 | 0.34 | 2015 |
| Domain Adaptation and Transfer Learning in StochasticNets | 0 | 0.34 | 2015 |
| Discovery Radiomics for Computed Tomography Cancer Detection | 3 | 0.51 | 2015 |
| Discovery Radiomics via StochasticNet Sequencers for Cancer Detection | 3 | 0.39 | 2015 |
| Apparent Ultra-High b-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields. | 0 | 0.34 | 2015 |
| Single-Click, Semi-Automatic Lung Nodule Contouring Using Hierarchical Conditional Random Fields | 1 | 0.36 | 2015 |
| StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity. | 8 | 0.74 | 2015 |
| Oil spill candidate detection from SAR imagery using a thresholding-guided stochastic fully-connected conditional random field model | 5 | 0.47 | 2015 |
| Dense Depth Map Reconstruction from Sparse Measurements Using a Multilayer Conditional Random Field Model | 0 | 0.34 | 2015 |
| Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework. | 2 | 0.41 | 2015 |
