10.9k citations
- Michigan State UniversityUS325 papers
- University of MichiganUS321 papers
- Joint Institute for Nuclear ResearchRU318 papers
- Centre National de la Recherche ScientifiqueFR314 papers
- Lawrence Berkeley National LaboratoryUS308 papers
- Institute for High Energy PhysicsES302 papers
- Fermi National Accelerator LaboratoryUS298 papers
- McGill UniversityCA298 papers
- Institute for Theoretical and Experimental PhysicsRU296 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR292 papers
- TRIUMFCA287 papers
- Sorbonne UniversitéFR279 papers
88 papers · 1 filter
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Mohammad Akbari, Jie Liang, Jingning Han +1
Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rat…
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
Arash Khoeini, Saman Haratizadeh, Ehsan Hoseinzade
Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works…
Pattern Morphing for Efficient Graph Mining
Kasra Jamshidi, Keval Vora
Graph mining applications analyze the structural properties of large graphs, and they do so by finding subgraph isomorphisms, which makes them computationally intensive. Existing g…
Real-Time Formal Verification of Autonomous Systems With An FPGA
Minh Bui, Michael Lu, Reza Hojabr +2
Hamilton-Jacobi reachability analysis is a powerful technique used to verify the safety of autonomous systems. This method is very good at handling non-linear system dynamics with…
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
Karen Leung, Edward Schmerling, Mengxuan Zhang +4
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is en…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…