Search Results for author:
Found 9 papers, 2 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Expectation-Complete Graph Representations with Homomorphisms
We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation.
Disentangled Representations using Trained Models
With the help of the implicit function theorem we show how, using a diverse set of models that have already been trained on the data, to select a pair of data points that have a common value of interpretable factors.
Corresponding Projections for Orphan Screening
We propose a novel transfer learning approach for orphan screening called corresponding projections.
Effective Parallelisation for Machine Learning
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications.
Scalable Learning in Reproducing Kernel Krein Spaces
We provide the first mathematically complete derivation of the Nystr\"om method for low-rank approximation of indefinite kernels and propose an efficient method for finding an approximate eigendecomposition of such kernel matrices.
Nyström Method with Kernel K-means++ Samples as Landmarks
We investigate, theoretically and empirically, the effectiveness of kernel K-means++ samples as landmarks in the Nyström method for low-rank approximation of kernel matrices.
Predicting Dynamic Difficulty
Motivated by applications in electronic games as well as teaching systems, we investigate the problem of dynamic difficulty adjustment.
