Technology
Fast Sparse Group Lasso
Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima
However,asan update ofonlyoneparameter group depends onalltheparameter groups ordata points, the computation cost is high when the number of the parameters or data points islarge. This paper proposes afast Block Coordinate Descent for Sparse GroupLasso.
Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods
The dataset is created to addressing a real-world challenge: creating a clean fine-grained taxonomy dataset.
Porcupine Neural Networks: Approximating Neural Network Landscapes
Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Neural Information Processing Systems http://nips.cc/
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Debraj Basu, Deepesh Data, Can Karakus, Suhas Diggavi
Neural Information Processing Systems http://nips.cc/
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Note that lower values of ϵ and δ correspond to stronger privacy.
Compiler Auto-Vectorization with Imitation Learning
Charith Mendis, Cambridge Yang, Yewen Pu, Dr.Saman Amarasinghe, Michael Carbin
Therefore, many existing SLP vectorization schemes are driven by hand-crafted heuristics (Liu et al., 2012;
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Neural Information Processing Systems http://nips.cc/
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more andmore details.