no code implementations • ICLR 2019 • Lili Meng, Bo Zhao, Bo Chang, Gao Huang, Frederick Tung, Leonid Sigal
Our model is efficient, as it proposes a separable spatio-temporal mechanism for video attention, while being able to identify important parts of the video both spatially and temporally.
Action Recognition In Videos Temporal Action Localization +1
no code implementations • 17 Nov 2022 • Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen
We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
no code implementations • 2 Apr 2022 • Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
Users who come to recommendation platforms are heterogeneous in activity levels.
no code implementations • 26 Jan 2022 • Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
2 code implementations • ICLR 2021 • Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei
In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information.
1 code implementation • NeurIPS 2020 • Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.
no code implementations • 24 Feb 2020 • Ruizhi Deng, Yanshuai Cao, Bo Chang, Leonid Sigal, Greg Mori, Marcus A. Brubaker
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence.
no code implementations • 18 Oct 2019 • Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, JiaWei He, Thibaut Durand, Marcus Brubaker, Greg Mori
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
no code implementations • 25 Sep 2019 • Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington
We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.
1 code implementation • ICLR 2019 • Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi
In this paper, we draw connections between recurrent networks and ordinary differential equations.
no code implementations • 25 Jan 2019 • Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington
We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.
no code implementations • 1 Oct 2018 • Lili Meng, Bo Zhao, Bo Chang, Gao Huang, Wei Sun, Frederich Tung, Leonid Sigal
Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition.
2 code implementations • ECCV 2018 • Bo Zhao, Bo Chang, Zequn Jie, Leonid Sigal
Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains.
1 code implementation • ICLR 2019 • Mai Zhu, Bo Chang, Chong Fu
A convolutional neural network can be constructed using numerical methods for solving dynamical systems, since the forward pass of the network can be regarded as a trajectory of a dynamical system.
3 code implementations • 25 Jan 2018 • Bo Chang, Qiong Zhang, Shenyi Pan, Lili Meng
Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values.
no code implementations • ICLR 2018 • Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert
Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks.
2 code implementations • 12 Sep 2017 • Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham
In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success.
Ranked #49 on Image Classification on STL-10