7 accepted papers
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, w…
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs.…
We propose automatic speech recognition (ASR) models inspired by echo state network (ESN) [1], in which a subset of recurrent neural networks (RNN) layers in the models are randomly initialized and untrained. Our study focuses on RNN-T and Conformer models, and we show that model quality does not dr…
Recognizing ongoing events based on acoustic clues has been a critical yet challenging problem that has attracted significant research attention in recent years. Joint audio-visual analysis can improve the event detection accuracy but may not always be feasible as under many circumstances only audio…
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formula…
Multi-label audio tagging is the task of predicting the types of sounds occurring in an audio clip. Recently, large-scale audio datasets such as Google's AudioSet, have allowed researchers to use deep learning techniques for this task but this comes at the cost of label noise in the datasets. Audio…
We propose a new approach, called cooperative neural networks (CoNN), which use a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family d…