Deep Learning
Restoring Negative Information in Few-Shot Object Detection Y ukuan Yang
This ability, however, is still a challenge for machine perception.
Q1 (R2): Motivation for parameterizing the score function explicitly, rather than as the gradient of an energy model
We thank all the reviewers for providing valuable feedback. In what follows, we address specific questions. The main motivation is computational. We will discuss this motivation in Section 2.1. Q2 (R2): Metrics or experiments to assess whether the model is overfitting or memorizing the dataset.
Cross-Scale Internal Graph Neural Network for Image Super-Resolution Shangchen Zhou
In this paper, we explore the cross-scale patch recurrence property of a natural image, i.e.,
Dual Manifold Adversarial Robustness: Defense against L p and non-L p Adversarial Attacks
However, it often degrades the model performance on normal images and more importantly, the defense does not generalize well to novel attacks.
Understanding and Improving Layer Normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin
Neural Information Processing Systems http://nips.cc/
Neural Adaptive Sequential Monte Carlo
Shixiang (Shane) Gu, Zoubin Ghahramani, Richard E. Turner
Neural Information Processing Systems http://nips.cc/
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu, Mehmet F. Demirel, Yingyu Liang
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering.
Adaptive Sequence Submodularity
Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi
Without further structure, finding an optimal sequence is notoriously challenging, if not completely intractable.
