Search Results for author: Bo Chang

Found 17 papers, 7 papers with code

Where and when to look? Spatial-temporal attention for action recognition in videos

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

Latent User Intent Modeling for Sequential Recommenders

no code implementations17 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.

Recommendation Systems

Recency Dropout for Recurrent Recommender Systems

no code implementations26 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.

Data Augmentation Recommendation Systems

CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks

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.

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

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.

Density Estimation Irregular Time Series +2

Variational Hyper RNN for Sequence Modeling

no code implementations24 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.

Time Series Time Series Analysis

Point Process Flows

no code implementations18 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.

Point Processes

The Dynamics of Signal Propagation in Gated Recurrent Neural Networks

no code implementations25 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.

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs

no code implementations25 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.

Interpretable Spatio-temporal Attention for Video Action Recognition

no code implementations1 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.

Action Recognition Temporal Action Localization

Modular Generative Adversarial Networks

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.

Attribute Image-to-Image Translation +1

Convolutional Neural Networks combined with Runge-Kutta Methods

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.

Image Classification

Generating Handwritten Chinese Characters using CycleGAN

3 code implementations25 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.

Multi-level Residual Networks from Dynamical Systems View

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.

General Classification Image Classification

Reversible Architectures for Arbitrarily Deep Residual Neural Networks

2 code implementations12 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.

Image Classification

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