Oceania
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.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Neural Information Processing Systems http://nips.cc/
Sparsified SGD with Memory
Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
Several papers consider approaches that limit the number of bits to represent floating point numbers [13, 24, 31]. Recent work proposes adaptive tuning of the compression ratio [7].
Discriminative Calibration: Check Bayesian
However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challenge, and the resulting p-value is not a divergence metric.
Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh
Choosing where to search matters. A time-tested path in the quest for new products or processes isthrough experimental optimization.
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang
Neural Information Processing Systems http://nips.cc/
