Squeeze-and-Attention Networks for Semantic Segmentation
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
State of Compact Architecture Search For Deep Neural Networks
Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms
Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage
EdgeSegNet: A Compact Network for Semantic Segmentation
Democratisation of Usable Machine Learning in Computer Vision
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Polyploidism in Deep Neural Networks: m-Parent Evolutionary Synthesis of Deep Neural Networks in Varying Population Sizes
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding
Dynamic Representations Toward Efficient Inference on Deep Neural Networks by Decision Gates
Assessing Architectural Similarity in Populations of Deep Neural Networks
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets
Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations
MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification
Tiny SSD: A Tiny Single-shot Detection Convolutional Neural Network for Real-time Embedded Object Detection
Nature vs. Nurture: The Role of Environmental Resources on Evolutionary Deep Intelligence
SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis
Featured on CNA's 'AI on AI' Show
The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks
Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks
Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection
Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks
' Most interesting paper' - MIT Technology Review, June 2016
Efficient Deep Feature Learning and Extraction via StochasticNets
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection