1 code implementation • 27 Nov 2021 • Alasdair Tran, Alexander Mathews, Lexing Xie, Cheng Soon Ong
We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs).
1 code implementation • 15 Feb 2021 • Alasdair Tran, Alexander Mathews, Cheng Soon Ong, Lexing Xie
We introduce Radflow, a novel model that embodies three key ideas: a recurrent neural network to obtain node embeddings that depend on time, the aggregation of the flow of influence from neighboring nodes with multi-head attention, and the multi-layer decomposition of time series.
no code implementations • 3 Feb 2021 • Minjeong Shin, Alasdair Tran, Siqi Wu, Alexander Mathews, Rong Wang, Georgiana Lyall, Lexing Xie
The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest.
no code implementations • 28 Jul 2020 • Alexander Soen, Alexander Mathews, Daniel Grixti-Cheng, Lexing Xie
The proof connects the well known Stone-Weierstrass Theorem for function approximation, the uniform density of non-negative continuous functions using a transfer functions, the formulation of the parameters of a piece-wise continuous functions as a dynamic system, and a recurrent neural network implementation for capturing the dynamics.
1 code implementation • CVPR 2020 • Alasdair Tran, Alexander Mathews, Lexing Xie
We address the first challenge by associating words in the caption with faces and objects in the image, via a multi-modal, multi-head attention mechanism.
1 code implementation • 6 Dec 2018 • Umanga Bista, Alexander Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie
This paper considers extractive summarisation in a comparative setting: given two or more document groups (e. g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups.
1 code implementation • CVPR 2018 • Alexander Mathews, Lexing Xie, Xuming He
We develop a model that learns to generate visually relevant styled captions from a large corpus of styled text without aligned images.
no code implementations • 15 May 2018 • Alexander Mathews, Lexing Xie, Xuming He
We simplify sentences with an attentive neural network sequence to sequence model, dubbed S4.
no code implementations • 6 Oct 2015 • Alexander Mathews, Lexing Xie, Xuming He
We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments.