Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.

Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.

Document retrieval plays an essential role in web search applications and various downstream knowledge-intensive tasks by identifying relevant documents to satisfy users' queries.

The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the single-swap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size.

Heterogeneous federated learning without assuming any structure is challenging due to the conflicts among non-identical data distributions of clients.

In CEED, we assume that waveforms are already extracted from an extracellular recording. Each waveform is then passed through our stochastic view generation module, where different views are obtained by applying transformations.

We formalize coherence --a requirement that erroneous predictions, within a slice, should be wrong for the same reason --as a key property that any slice discovery method should satisfy.

This means that the algorithm designer needs to balance several conflicting desiderata.