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PeleNet: A Reservoir Computing Framework for Loihi
CarloMichaelis....
Published date-11/24/2020
High-level frameworks for spiking neural networks are a key factor for fast prototyping and efficient development of complex algorithms. Such frameworks have emerged in the last years for traditional computers, …
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
MaosenZhang, NanJiang, LeiLI....
Published date-11/24/2020
LanguageModelling, TextGeneration
Generating natural language under complex constraints is a principled formulation towards controllable text generation. We present a framework to allow specification of combinatorial constraints for sentence generation. We propose TSMH, …
Play Fair: Frame Attributions in Video Models
WillPrice, DimaDamen....
Published date-11/24/2020
ActionRecognition, RelationalReasoning
In this paper, we introduce an attribution method for explaining action recognition models. Such models fuse information from multiple frames within a video, through score aggregation or relational reasoning. We …
Augmented Lagrangian Adversarial Attacks
JérômeRony, EricGranger, MarcoPedersoli....
Published date-11/24/2020
AdversarialAttack
Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the properties of the considered distance. We …
Dissecting Image Crops
BasileVanHoorick, CarlVondrick....
Published date-11/24/2020
DataAugmentation, ImageForensics, RepresentationLearning, Self-SupervisedLearning
The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces …
Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning
SohamGadgil, YunfengXin, ChengzheXu....
Published date-11/24/2020
Q-Learning
Reinforcement Learning (RL) is an area of machine learning concerned with enabling an agent to navigate an environment with uncertainty in order to maximize some notion of cumulative long-term reward. …