Co-occurring keywords · papers mentioning both
Papers
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
AAAI 2020
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
ICLR 2020
Towards Unified INT8 Training for Convolutional Neural Network
CVPR 2020
Training-Time-Friendly Network for Real-Time Object Detection
AAAI 2020
Train Once, and Decode As You Like
COLING 2020
UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa
EMNLP 2020
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
WACV 2020
Ultra-Low Precision 4-bit Training of Deep Neural Networks
NeurIPS 2020
Understanding and Exploring the Network with Stochastic Architectures
NeurIPS 2020
Understanding the Disharmony between Weight Normalization Family and Weight Decay
AAAI 2020
Understanding Why Neural Networks Generalize Well Through GSNR of Parameters
ICLR 2020
Up or Down? Adaptive Rounding for Post-Training Quantization
ICML 2020
VECA: A Method for Detecting Overfitting in Neural Networks (Student Abstract)
AAAI 2020
What Makes Training Multi-Modal Classification Networks Hard?
CVPR 2020
Wide Hidden Expansion Layer for Deep Convolutional Neural Networks
WACV 2020
Acoustic Model Optimization Based on Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
Interspeech 2019
Adversarial Robustness vs. Model Compression, or Both?
ICCV 2019
A Kernel Perspective for Regularizing Deep Neural Networks
ICML 2019
All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
CVPR 2019
A Mean Field Theory of Batch Normalization
ICLR 2019
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
NeurIPS 2019
Analysis of Quantized Models
ICLR 2019
Analyzing Federated Learning through an Adversarial Lens
ICML 2019
An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression
AAAI 2019
An Empirical study of Binary Neural Networks' Optimisation
ICLR 2019