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SUTSEC: SDN Utilized trust based secure clustering in IoT
(Elsevier, 2020-09-04)
Internet of Things (IoT) technology consists of huge number of heterogeneous devices that create enormous amount of data. Providing a robust communication for billions of devices is one of the most significant challenges ...
A benchmark for inpainting of clothing images with irregular holes
(Springer, 2020)
Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has ...
Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020)
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020)
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ...
Probabilistic logic programming with beta-distributed random variables
(Association for the Advancement of Artificial Intelligence, 2019-07-17)
We enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms ...
Sampling-free variational inference of bayesian neural networks by variance backpropagation
(ML Research Press, 2020)
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ...
Sampling-free variational inference of Bayesian neural networks by variance backpropagation
(Association For Uncertainty in Artificial Intelligence (AUAI), 2019)
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ...
OzU-NLP at TREC NEWS 2019: Entity ranking
(National Institute of Standards and Technology (NIST), 2019)
This paper presents our work and submission for TREC 2019 News Track: Entity Ranking Task. Our approach utilizes Doc2Vec's ability to represent documents as fixed sized numerical vectors. Applied on news articles and ...
Language inference with multi-head automata through reinforcement learning
(IEEE, 2020)
The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple ...
DPSec: A blockchain-based data plane authentication protocol for SDNs
(IEEE, 2020-11-02)
Software-Defined Networking (SDN) is a promising networking architecture that enables central management along with network programmability. However, SDN brings additional security threats due to untrusted control and data ...
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