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Found 6 papers, 5 papers with code
Latent SDEs on Homogeneous Spaces
We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).
On Measuring Excess Capacity in Neural Networks
We study the excess capacity of deep networks in the context of supervised classification.
Topological Attention for Time Series Forecasting
The problem of (point) forecasting $ \textit{univariate} $ time series is considered.
Dissecting Supervised Contrastive Learning
Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks.
Topologically Densified Distributions
We study regularization in the context of small sample-size learning with over-parameterized neural networks.
Graph Filtration Learning
We propose an approach to learning with graph-structured data in the problem domain of graph classification.
