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Found 18 papers, 4 papers with code
Meta Reinforcement Learning with Task Embedding and Shared Policy
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
Adversarial Example Detection by Classification for Deep Speech Recognition
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks.
Vehicle Traffic Driven Camera Placement for Better Metropolis Security Surveillance
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism.
Fast Generating A Large Number of Gumbel-Max Variables
Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations
Graph convolution network (GCN) attracts intensive research interest with broad applications.
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings
In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort.
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features.
Cryptography and Security Software Engineering
Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder
While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing.
A Review of Deep Reinforcement Learning for Smart Building Energy Management
However, it is a nontrivial task due to the following challenges.
Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning
To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR).
Uncertainty Set Prediction of Aggregated Wind Power Generation based on Bayesian LSTM and Spatio-Temporal Analysis
This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.
A General Formulation for Evaluating the Performance of Linear Power Flow Models
Various LPF models are proposed, but some crucial questions are still remained: what is the performance bound (e. g., the error bound) of LPF models, how to know a branch is applicable for LPF models or not, and what is the best LPF model.
Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach
We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs.
A Two-phase On-line Joint Scheduling for Welfare Maximization of Charging Station
First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers.
Federated Learning over Coupled Graphs
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
Fast Gumbel-Max Sketch and its Applications
The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval.
Model-Free Load Frequency Control of Nonlinear Power Systems Based on Deep Reinforcement Learning
Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality.
