Deep Learning
Learning Hierarchical Semantic Image Manipulation through Structured Representations
Seunghoon Hong, Xinchen Yan, Thomas S. Huang, Honglak Lee
Neural Information Processing Systems http://nips.cc/
Incremental Scene Synthesis
Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, YingLi Tian, Jan Ernst, ANDREAS HUTTER
Novel view synthesis methods of this type have three common limitations.
Overleaf Example
Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.
Hy pergraph-enhanced Tabular Data Representation Learning Pei Chen
Tabular data that is organized in bi-dimensional matrices are widespread in webpages, documents, and databases.
Referring Human Pose and Mask Estimation in the Wild Bo Miao
This new task holds significant potential for human-centric applications such as assistive robotics and sports analysis.
PruningNeuralNetworksviaCoresetsandConvex Geometry: TowardsNoAssumptions
Pruning is one of the predominant approaches for compressing deep neural networks (DNNs).
PruningNeuralNetworksviaCoresetsandConvex Geometry: TowardsNoAssumptions
Pruning is one of the predominant approaches for compressing deep neural networks (DNNs).
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications Robert Tjarko Lange
Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g.