Country
Unsupervised Learning of View-invariant Action Representations
Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Neural Information Processing Systems http://nips.cc/
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the
TowardsVideoTextVisualQuestionAnswering: BenchmarkandBaseline
Therearealready sometext-based visualquestion answering(TextVQA) benchmarks for developing machine's ability to answer questions based on texts in imagesinrecentyears.
Inferring Networks From Random Walk-Based Node Similarities
Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis
Neural Information Processing Systems http://nips.cc/
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cedric Archambeau, Rodolphe Jenatton
Neural Information Processing Systems http://nips.cc/
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Codes and the dataset are available on the website.
Memory-oriented Decoder for Light Field Salient Object Detection
Miao Zhang, Jingjing Li, JI WEI, Yongri Piao, Huchuan Lu
Neural Information Processing Systems http://nips.cc/
Efficient Formal Safety Analysis of Neural Networks
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana
InProceedingsofthe Networkand Distributed System Security Symposium, volume 14, pages 23-26, 2014.
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.