activity
20122024
most citedApplying Deep Belief Networks to Word Sense Disambiguation

12 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Mixtures of Unsupervised Lexicon Classification

Peratham Wiriyathammabhum

This paper presents a mixture version of the method-of-moment unsupervised lexicon classification by an incorporation of a Dirichlet process.

cs.CL2021

Is Sluice Resolution really just Question Answering?

Peratham Wiriyathammabhum

Sluice resolution is a problem where a system needs to output the corresponding antecedents of wh-ellipses. The antecedents are elided contents behind the wh-words but are implicit…

cs.CV2020

SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading

Peratham Wiriyathammabhum

This paper presents a novel deep learning architecture for word-level lipreading. Previous works suggest a potential for incorporating a pretrained deep 3D Convolutional Neural Net…

cs.CV2019

Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions

Peratham Wiriyathammabhum, Abhinav Shrivastava, Vlad I. Morariu +1

This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the n…

cs.AI20122 cited

Robust Principal Component Analysis Using Statistical Estimators

Peratham Wiriyathammabhum, Boonserm Kijsirikul

Principal Component Analysis (PCA) finds a linear mapping and maximizes the variance of the data which makes PCA sensitive to outliers and may cause wrong eigendirection. In this p…

cs.CL201212 cited

Applying Deep Belief Networks to Word Sense Disambiguation

Peratham Wiriyathammabhum, Boonserm Kijsirikul, Hiroya Takamura +1

In this paper, we applied a novel learning algorithm, namely, Deep Belief Networks (DBN) to word sense disambiguation (WSD). DBN is a probabilistic generative model composed of mul…