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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, …
GLGE: A New General Language Generation Evaluation Benchmark
DayihengLiu, YuYan, YeyunGong....
Published date-11/24/2020
NaturalLanguageUnderstanding, TextGeneration, TransferLearning
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural …
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. …
Benchmarking Image Retrieval for Visual Localization
NoéPion, MartinHumenberger, GabrielaCsurka....
Published date-11/24/2020
AutonomousDriving, ImageRetrieval, PoseEstimation, VisualLocalization
Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image …
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 …