5 accepted papers
Current audio-visual speech separation (AVSS) models typically rely on implicit multimodal fusion, but the absence of explicit modality alignment and reliability modeling often causes semantic misalignment and contaminates speech representations. The brain addresses this with a hierarchy: top-down a…
Modeling nonlinear dynamical systems is a challenging task in fields such as speech processing, music generation, and video prediction. This paper introduces a hierarchical framework for Deep State Space Models (DSSMs), categorizing them by their conditional independence properties and Markov assump…
Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and produce ambiguous queries. Responses based on misaligned assumptions may be perceived as hallucinations. Therefore, ide
This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Build…
In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps—although…