3k citations
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT941 papers
- Peking UniversityCN882 papers
- Institute of High Energy PhysicsCN790 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PerugiaIT756 papers
- Tsinghua UniversityCN749 papers
- Massachusetts Institute of TechnologyUS745 papers
- National Centre for Nuclear ResearchPL737 papers
- Fermi National Accelerator LaboratoryUS721 papers
- Beihang UniversityCN708 papers
- Joint Institute for Nuclear ResearchRU702 papers
- The Ohio State UniversityUS701 papers
- University of TurinIT683 papers
98 papers · 2 filters
Pseudo-Encoded Stochastic Variational Inference
Amir Zadeh, Smon Hessner, Yao-Chong Lim +1
Posterior inference in directed graphical models is commonly done using a probabilistic encoder (a.k.a inference model) conditioned on the input. Often this inference model is trai…
Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks
Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu
The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understandi…
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
Rahul Radhakrishnan Iyer, Jing Chen, Haonan Sun +1
Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for op…
Pairwise Feedback for Data Programming
Benedikt Boecking, Artur Dubrawski
The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of la…
Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu +4
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such trai…
Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Menghan Wang, Kun Zhang, Gulin Li +2
Graph Convolutional Networks (GCNs) have gained significant developments in representation learning on graphs. However, current GCNs suffer from two common challenges: 1) GCNs are…