_ | relu | _ |
Approximating | relu | Networks by Single-Spike Computation |
AutoReP: Automatic | relu | Replacement for Fast Private Network Inference |
Blues: Before- | relu | -estimates Bayesian Inference for Crowd Counting |
Depth Selection for Deep | relu | Nets in Feature Extraction and Generalization |
Driver Mental Fatigue Detection Based on Head Posture Using New Modified | relu | -BiLSTM Deep Neural Network |
Dynamic | relu | |
Global-connected network with generalized | relu | activation |
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on | relu | networks |
L* | relu | : Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization |
Lightweight | relu | -Based Feature Fusion for Aerial Scene Classification, A |
PEA: Improving the Performance of | relu | Networks for Free by Using Progressive Ensemble Activations |
Randomized Gradient-Free Attack on | relu | Networks, A |
Refinement and Universal Approximation via Sparsely Connected | relu | Convolution Nets |
Rethinking | relu | to Train Better CNNs |
Total contribution score and fuzzy entropy based two-stage selection of FC, | relu | and inverseReLU features of multiple convolution neural networks for erythrocytes detection |
Why | relu | Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem |
16 for relu