Search Results for author: Chen Liang

Found 55 papers, 19 papers with code

Evolving Machine Learning Algorithms From Scratch

no code implementations ICML 2020 Esteban Real, Chen Liang, David So, Quoc Le

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.

AutoML

Interface Networks for Failure Localization in Power Systems

no code implementations12 May 2022 Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman

Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently.

CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing

1 code implementation ACL 2022 Chen Liang, Pengcheng He, Yelong Shen, Weizhu Chen, Tuo Zhao

To retain ensemble benefits while maintaining a low memory cost, we propose a consistency-regularized ensemble learning approach based on perturbed models, named CAMERO.

Ensemble Learning

Visual Abductive Reasoning

1 code implementation26 Mar 2022 Chen Liang, Wenguan Wang, Tianfei Zhou, Yi Yang

In this paper, we propose a new task and dataset, Visual Abductive Reasoning (VAR), for examining abductive reasoning ability of machine intelligence in everyday visual situations.

Local-Global Context Aware Transformer for Language-Guided Video Segmentation

1 code implementation18 Mar 2022 Chen Liang, Wenguan Wang, Tianfei Zhou, Jiaxu Miao, Yawei Luo, Yi Yang

In light of this, we present Locater (local-global context aware Transformer), which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner.

Frame Semantic Segmentation +4

Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

no code implementations18 Feb 2022 Yuyang Wang, Rishikesh Magar, Chen Liang, Amir Barati Farimani

On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.

Contrastive Learning Self-Supervised Learning

TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model

no code implementations9 Jan 2022 Chunnan Wang, Chen Liang, Xiang Chen, Hongzhi Wang

They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results.

Anomaly Detection AutoML +1

A General Traffic Shaping Protocol in E-Commerce

no code implementations30 Dec 2021 Chenlin Shen, Guangda Huzhang, YuHang Zhou, Chen Liang, Qing Da

Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

1 code implementation30 Nov 2021 Rishikesh Magar, Yuyang Wang, Cooper Lorsung, Chen Liang, Hariharan Ramasubramanian, Peiyuan Li, Amir Barati Farimani

Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.

Data Augmentation

Contrastive Video-Language Segmentation

no code implementations29 Sep 2021 Chen Liang, Yawei Luo, Yu Wu, Yi Yang

We focus on the problem of segmenting a certain object referred by a natural language sentence in video content, at the core of formulating a pinpoint vision-language relation.

Contrastive Learning

Self-Training with Differentiable Teacher

no code implementations15 Sep 2021 Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha

In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.

Named Entity Recognition Weakly Supervised Classification

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

no code implementations CVPR 2021 Jiaxu Miao, Yunchao Wei, Yu Wu, Chen Liang, Guangrui Li, Yi Yang

To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios.

Frame Scene Parsing

Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization

1 code implementation ACL 2021 Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen

The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i. e., a subnetwork) can match the performance of the full model.

Model Compression Multi-Task Learning

Carbon Emissions and Large Neural Network Training

no code implementations21 Apr 2021 David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean

To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.

Neural Architecture Search

Token-wise Curriculum Learning for Neural Machine Translation

no code implementations Findings (EMNLP) 2021 Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Tuo Zhao

Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of "easy" samples from training data at the early training stage.

Machine Translation Translation

ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation

no code implementations19 Mar 2021 Chen Liang, Yu Wu, Yawei Luo, Yi Yang

Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos.

Referring Expression Segmentation Video Segmentation +2

LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign

no code implementations3 Feb 2021 Tianchi Cai, Daxi Cheng, Chen Liang, Ziqi Liu, Lihong Gu, Huizhi Xie, Zhiqiang Zhang, Xiaodong Zeng, Jinjie Gu

In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.

Link Prediction

Compositional Generalization via Neural-Symbolic Stack Machines

no code implementations NeurIPS 2020 Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou

Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply them to unseen cases in a systematic manner.

Few-Shot Learning Machine Translation +1

Line Failure Localization of Power Networks Part II: Cut Set Outages

no code implementations22 May 2020 Linqi Guo, Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman

Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult.

Adaptive Network Response to Line Failures in Power Systems

no code implementations22 May 2020 Chen Liang, Linqi Guo, Alessandro Zocca, Steven H. Low, Adam Wierman

Transmission line failures in power systems propagate and cascade non-locally.

Line Failure Localization of Power Networks Part I: Non-cut Outages

no code implementations20 May 2020 Linqi Guo, Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman

Transmission line failures in power systems propagate non-locally, making the control of the resulting outages extremely difficult.

Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension

no code implementations ICLR 2020 Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le

Integrating distributed representations with symbolic operations is essential for reading comprehension requiring complex reasoning, such as counting, sorting and arithmetics, but most existing approaches are hard to scale to more domains or more complex reasoning.

Data Augmentation Question Answering +1

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

2 code implementations6 Mar 2020 Esteban Real, Chen Liang, David R. So, Quoc V. Le

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.

AutoML

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing

1 code implementation ACL 2020 Haoming Jiang, Chen Liang, Chong Wang, Tuo Zhao

To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing.

Machine Translation Transfer Learning +1

Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control

no code implementations15 Apr 2019 Chen Liang, Weihong Wang, Zhenghua Liu, Chao Lai, Benchun Zhou

However the traditional MPPI framework assumes the actual environment similar to the training dataset for the deep neural network which is impractical in practice with different maneuvering of target, other perturbations and actuator failures.

Robotics Systems and Control

Neural Program Planner for Structured Predictions

no code implementations ICLR Workshop drlStructPred 2019 Jacob Biloki, Chen Liang, Ni Lao

We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) – for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and receives a binary success-failure reward.

Machine Translation Program Synthesis +3

Learning to Generalize from Sparse and Underspecified Rewards

1 code implementation19 Feb 2019 Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy.

Semantic Parsing

The Evolved Transformer

2 code implementations30 Jan 2019 David R. So, Chen Liang, Quoc V. Le

Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models.

Machine Translation Neural Architecture Search

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

4 code implementations NeurIPS 2018 Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, Ni Lao

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

Combinatorial Optimization Program Synthesis +2

Distractor Generation for Multiple Choice Questions Using Learning to Rank

1 code implementation WS 2018 Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, C. Lee Giles

We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.

Distractor Generation Ensemble Learning +2

A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

no code implementations10 Nov 2017 Junting Zhang, Chen Liang, C. -C. Jay Kuo

We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images.

Domain Adaptation Scene Segmentation

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

no code implementations11 Sep 2017 Liang Tingting, He Lifang, Lu Chun-Ta, Chen Liang, Yu Philip S., Wu Jian

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users.

Feature Importance

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)

no code implementations4 Dec 2016 Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao

In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface.

Feature Engineering Natural Language Understanding +2

Definition Modeling: Learning to define word embeddings in natural language

2 code implementations1 Dec 2016 Thanapon Noraset, Chen Liang, Larry Birnbaum, Doug Downey

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks.

Word Embeddings Word Similarity

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

2 code implementations ACL 2017 Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base.

Feature Engineering Structured Prediction

A neural probabilistic model for context based citation recommendation

no code implementations AAAI 2015 Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles

It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context.

Citation Recommendation

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