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Found 7 papers, 1 papers with code
Challenges Faced by Large Language Models in Solving Multi-Agent Flocking
Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation.
Few-Shot Domain Adaptation with Polymorphic Transformers
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
Manifold Learning via Manifold Deflation
Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data.
Optimal Subsampling with Influence Functions
Subsampling is a common and often effective method to deal with the computational challenges of large datasets.
On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding
There is limited basis for using existing NLDR theory for deriving new algorithms.
Optimal Sub-sampling with Influence Functions
Sub-sampling is a common and often effective method to deal with the computational challenges of large datasets.
Adaptive Threshold Sampling
In particular, we design a sampling procedure for the top-K problem where, unlike in the heavy-hitter problem, the sketch size and sampling probabilities are adaptively chosen.
