Europe
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Codes and the dataset are available on the website.
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings Appendix
We provide hyper-parameters of our models in Table A.1. Table A.1: Hyper-parameters used for training our VisualCSE and AudioCSE. Vision, we use Dropout augmentation (the same strategy in SimCSE) for AudioCSE. We compare unsup-SimCSE and unsup-VisualCSE on a small scale retrieval test. As shown in Table C.1, VisualCSE generally retrieves qualitatively different sentences than SimCSE.
Lookback Prophet Inequalities
We analyze how lookback improves, or not, the competitive ratio in prophet inequalities in different order models.
DeepSetPredictionNetworks
Concretely,wecontributethefollowing: 1. Wepropose amodel (section 3, Algorithm 1) that can predict aset from afeature vector (vector-to-set) while properly taking the structure of sets into account.
Riemannian batch normalization for SPD neural networks
Daniel Brooks, Olivier Schwander, Frederic Barbaresco, Jean-Yves Schneider, Matthieu Cord
Inourarticle, weintroduce aRiemannian batch normalization(batchnorm) algorithm, which generalizes the one used in Euclidean nets.
