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Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
VitalyKurin, SaadGodil, ShimonWhiteson....
Published date-12/01/2020
FeatureEngineering, Q-Learning
We present Graph-Q-SAT, a branching heuristic for a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation. Solvers using Graph-Q-SAT are complete SAT …
FFD: Fast Feature Detector
MortezaGhahremani, YonghuaiLiu, BernardTiddeman....
Published date-12/01/2020
Scale-invariance, good localization and robustness to noise and distortions are the main properties that a local feature detector should possess. Most existing local feature detectors find excessive unstable feature points …
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
SaschaSaralajew, LarsHoldijk, ThomasVillmann....
Published date-12/01/2020
Quantization
Methods for adversarial robustness certification aim to provide an upper bound on the test error of a classifier under adversarial manipulation of its input. Current certification methods are computationally expensive …
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Keypoints for Indoor Localization
DongjiangLi, JinyuMiao, XuesongShi....
Published date-12/01/2020
IndoorLocalization, VisualLocalization
Image keypoint extraction is an important step for visual localization. The localization in indoor environment is challenging for that there may be many unreliable features on dynamic or repetitive objects. …
Lipschitz-Certifiable Training with a Tight Outer Bound
SungyoonLee, JaewookLee, SaeromPark....
Published date-12/01/2020
Verifiable training is a promising research direction for training a robust network. However, most verifiable training methods are slow or lack scalability. In this study, we propose a fast and …
Analysis of Drifting Features
FabianHinder, JonathanJakob, BarbaraHammer....
Published date-12/01/2020
FeatureSelection
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, …