Neural Information Processing Systems http://nips.cc/

In this paper, we present MixV al, an innovative model selection method that operates solely with unlabeled target data during inference.

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters.

In this paper, we make the first attempt to study Meta Architecture Search which aims at learning a task-agnostic representation that can be used to speed up the processof architecture search on alarge number of tasks.

We show how to "compile" human-readable programs into standard decoder-only transformer models.

Neural Information Processing Systems http://nips.cc/

A procedure of IHT consists of two main parts in an iteration.

However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (edges) and node information (attributes) can be jointly leveraged to design high-performance clustering algorithms. Under a general model for the network and node attributes, this work establishes an information-theoretic criterion for the exact recovery of community labels and characterizes a phase transition determined by the Chernoff-Hellinger divergence of the model.

Neural Information Processing Systems http://nips.cc/