31 citations
- Simon Fraser UniversityCA4 papers
- University of British ColumbiaCA3 papers
- Google (United States)US2 papers
- Brown UniversityUS1 paper
- Centrum Wiskunde & InformaticaNL1 paper
- Imperial College LondonGB1 paper
- Institute for Cognitive Science StudiesIR1 paper
- McGill UniversityCA1 paper
- North Carolina State UniversityUS1 paper
- University of TorontoCA1 paper
- University of WaterlooCA1 paper
- Vector InstituteCA1 paper
6 papers · 1 filter
TD-GEN: Graph Generation With Tree Decomposition
Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi +1
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation. The…
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this pr…
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding +2
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to se…
Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang, Alireza Makhzani, Yanshuai Cao +1
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood i…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
Graph Generation with Variational Recurrent Neural Network
Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurren…