DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification https://arxiv.org/abs/1812.11478
arXiv.org
DART: Domain-Adversarial Residual-Transfer Networks for...
The accuracy of deep learning (e.g., convolutional neural networks) for an
image classification task critically relies on the amount of labeled training
data. Aiming to solve an image...
image classification task critically relies on the amount of labeled training
data. Aiming to solve an image...
Feature Denoising for Improving Adversarial Robustness https://arxiv.org/abs/1812.03411
arXiv.org
Feature Denoising for Improving Adversarial Robustness
Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on...
Improving fairness in machine learning systems: What do industry practitioners need? https://arxiv.org/abs/1812.05239
arXiv.org
Improving fairness in machine learning systems: What do industry...
The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the...
A Theoretical Analysis of Deep Q-Learning https://arxiv.org/abs/1901.00137
arXiv.org
A Theoretical Analysis of Deep Q-Learning
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep...
An Experimental Course on Operating Systems https://web.stanford.edu/class/cs140e/
A Comprehensive Survey on Graph Neural Networks https://arxiv.org/abs/1901.00596
arXiv.org
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The...
Forwarded from Deep Learning
NeurIPS 2018 Paper Summary and Categorization on Reinforcement Learning 👉🏻
Medium
NeurIPS 2018 Reinforcement Learning Summary
This is your one stop shop for everything RL at NeurIPS 2018
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context -- SOTA on 5 datasets https://arxiv.org/abs/1901.02860
arXiv.org
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture...
Panoptic Feature Pyramid Networks https://arxiv.org/abs/1901.02446
arXiv.org
Panoptic Feature Pyramid Networks
The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff...
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation https://arxiv.org/abs/1901.02985
arXiv.org
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic...
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS...