
- Read more about TRACKING THE DIMENSIONS OF LATENT SPACES OF GAUSSIAN PROCESS LATENT VARIABLE MODELS
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Deep neural networks (DNNs) allow digital receivers to learn operating in complex environments.
DNNs infer reliably when applied in a similar statistical relationship as the one under which it was trained.
This property constitutes a major drawback of using DNN-aided receivers for dynamic communication systems, whose input-output relationship varies over time.
In such setups, DNN-aided receivers may be required to retrain periodically, which conventionally involves excessive pilot signaling at the cost of reduced spectral efficiency.
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- Read more about SCALABLE REINFORCEMENT LEARNING FOR ROUTING IN AD-HOC NETWORKS BASED ON PHYSICAL-LAYER ATTRIBUTES
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This work proposes a novel and scalable reinforcement learning approach for routing in ad-hoc wireless networks. In most previous reinforcement learning based routing methods, the links in the network are assumed to be fixed, and a different agent is trained for
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- Read more about SCORE-BASED CHANGE DETECTION FOR GRADIENT-BASED LEARNING MACHINES
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- Read more about A Unified Approach to Translate Classical Bandit algorithms to Structured Bandits
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- Read more about SELF-INFERENCE OF OTHERS' POLICIES FOR HOMOGENEOUS AGENTS IN COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING
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- Read more about POLA: Online Time Series Prediction by Adaptive Learning Rates
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Online prediction for streaming time series data has practical use for many real-world applications where downstream decisions depend on accurate forecasts for the future. Deployment in dynamic environments requires models to adapt quickly to changing data distributions without overfitting. We propose POLA (Predicting Online by Learning rate Adaptation) to automatically regulate the learning rate of recurrent neural network models to adapt to changing time series patterns across time.
POLA_poster.pdf

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- Read more about Optimum Feature Ordering for Dynamic Instance–wise Joint Feature Selection and Classification
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poster.pdf

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- Read more about STEP-GAN: A One-Class Anomaly Detection Model with Applications to Power System Security
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Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multi-generator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task.
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- Read more about Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors
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Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it’s power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and learns its own policy without considering others’ policies is a simple way to apply DRL to multi-agent tasks. However, since agents’ policies change as learning proceeds, from the perspective of each agent, the environment is non-stationary, which makes conventional DRL methods inefficient.
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