Search Results for author:
Found 5 papers, 2 papers with code
Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space
We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small dimensional feature space representing such a manifold than on the original pixel-space.
Gradient Boosting Neural Networks: GrowNet
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''.
Geometry Aware Mappings for High Dimensional Sparse Factors
While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors.
Generalized Linear Models for Aggregated Data
We consider a limiting case of generalized linear modeling when the target variables are only known up to permutation, and explore how this relates to permutation testing; a standard technique for assessing statistical dependency.
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation.
