Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics ofobservedsequence data.

We exemplify our result in two tasks of interest in statistical learning: a) classification for a mixture with sparse means, wherewestudytheefficiencyof `1penaltywithrespectto `2;b)max-marginmulticlass classification, where we characterise the phase transition on the existence ofthemulti-class logistic maximum likelihood estimator forK >2.

TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout.

How are sensory representations learned via experience? Deep learning offers a theoretical toolkit for studying how neural codes emerge under different learning rules.

Somemodelcompression methods have been successfully applied to image classification and detection or language models, but there has been very little work compressing generative adversarial networks(GANs) performing complextasks.

ItisshownthatQKFormer achieves significantly superior performance over existing state-of-the-art SNN models on various mainstream datasets.

In this work, we propose Glow-TTS, a flow-based generativemodel for parallel TTS that does not require anyexternal aligner.