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Found 12 papers, 5 papers with code
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
$f$-GAIL: Learning $f$-Divergence for Generative Adversarial Imitation Learning
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
Deceptive Kernel Function on Observations of Discrete POMDP
This paper studies the deception applied on agent in a partially observable Markov decision process.
Modeling Personalized Item Frequency Information for Next-basket Recommendation
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
Ranked #1 on
Next-basket recommendation
on TaFeng
Physics-Guided Deep Neural Networks for Power Flow Analysis
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning.
Ranked #3 on
Graph Classification
on COLLAB
(using extra training data)
Stability and Generalization of Graph Convolutional Neural Networks
In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting.
Graph Capsule Convolutional Neural Networks
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision.
Ranked #22 on
Graph Classification
on NCI1
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation.
Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships
Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing.
Social and Information Networks Discrete Mathematics Physics and Society E.1; H.3.3
