In this paper, we present a novel large vision-language model, TabPedia, equipped with aconcept synergy mechanism.

Moreover,ourtheoretical analysis reliesonstandard assumptions only,works inthedistributed heterogeneous data setting, and leads tobetter and more meaningful rates.

Work done during an internship at IDEA.

Flow models have recently made great progress at modeling ordinal discrete data such as images and audio.

However,DBNsonly allow for a fixed chain of Markov dependencies between time slices and cannot represent nested hierarchicalstructures.

In recent centralized nonconvex distributed learning and federated learning, localmethods areoneofthepromising approaches toreducecommunication time. However, existing work has mainly focused on studying first-order optimality guarantees. On the other side, second-order optimality guaranteed algorithms, i.e., algorithms escaping saddle points, havebeen extensivelystudied inthenondistributed optimization literature.

Specifically, we show that contrary to sets, elements in a graph are not entirely unordered and there is a unique partial order for nodes and edges. With this partial order,PARD generates a graph in a block-by-block, autoregressivefashion, where each block'sprobability isconditionally modeled by a shared diffusion model with an equivariant network.