565 citations · 1.7k across the 68 of their papers we have counts for
11 papers · 1 filter
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering
Hui Han Chin, Paul Pu Liang
The power of randomized algorithms in numerical methods have led to fast solutions which use the Singular Value Decomposition (SVD) as a core routine. However, given the large data…
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
Hai Pham, Paul Pu Liang, Thomas Manzini +2
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimod…
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Yansen Wang, Ying Shen, Zhun Liu +3
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on di…
Multimodal Local-Global Ranking Fusion for Emotion Recognition
Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
Emotion recognition is a core research area at the intersection of artificial intelligence and human communication analysis. It is a significant technical challenge since humans di…
Multimodal Language Analysis with Recurrent Multistage Fusion
Paul Pu Liang, Ziyin Liu, Amir Zadeh +1
Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending…
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Hai Pham, Thomas Manzini, Paul Pu Liang +1
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representa…
Learning Factorized Multimodal Representations
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh +2
Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multi…
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan +3
Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the…
Memory Fusion Network for Multi-view Sequential Learning
Amir Zadeh, Paul Pu Liang, Navonil Mazumder +3
Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions betw…
Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning
Minghai Chen, Sen Wang, Paul Pu Liang +3
With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community…
Multi-attention Recurrent Network for Human Communication Comprehension
Amir Zadeh, Paul Pu Liang, Soujanya Poria +3
Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our…