Search Results for author: Lu Liu

Found 37 papers, 15 papers with code

Distributed Optimal Output Consensus of Uncertain Nonlinear Multi-Agent Systems over Unbalanced Directed Networks via Output Feedback

no code implementations16 Nov 2021 Jin Zhang, Lu Liu, Xinghu Wang, Haibo Ji

In this note, a novel observer-based output feedback control approach is proposed to address the distributed optimal output consensus problem of uncertain nonlinear multi-agent systems in the normal form over unbalanced directed graphs.

Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

no code implementations ICLR 2022 Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang

The size of the receptive field has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks.

Time Series Time Series Classification

Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision

no code implementations30 Aug 2021 Bo Li, Xinyang Jiang, Donglin Bai, Yuge Zhang, Ningxin Zheng, Xuanyi Dong, Lu Liu, Yuqing Yang, Dongsheng Li

The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change.

Model Compression

Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception

1 code implementation18 May 2021 Ridong Han, Tao Peng, Jiayu Han, Hai Cui, Lu Liu

Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels.

Relation Extraction

Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior

no code implementations7 May 2021 Lu Liu, Robby T. Tan

At inference, we propose a human-object regrouping approach by considering the object-exclusive property of an action, where the target object should not be shared by more than one human.

Human-Object Interaction Detection

FedProto: Federated Prototype Learning across Heterogeneous Clients

1 code implementation1 May 2021 Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.

Federated Learning

Isometric Propagation Network for Generalized Zero-shot Learning

no code implementations ICLR 2021 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang

To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.

Generalized Zero-Shot Learning

Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task

no code implementations24 Jan 2021 Xiaohan Zhang, Lu Liu, Guodong Long, Jing Jiang, Shenquan Liu

Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.

Decision Making Hippocampus +1

Free Lunch for Few-shot Learning: Distribution Calibration

2 code implementations ICLR 2021 Shuo Yang, Lu Liu, Min Xu

In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier.

Few-Shot Learning

Real-Time AutoML

no code implementations1 Jan 2021 Iddo Drori, Brandon Kates, Anant Kharkar, Lu Liu, Qiang Ma, Jonah Deykin, Nihar Sidhu, Madeleine Udell

We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset.

AutoML Representation Learning

MASP: Model-Agnostic Sample Propagation for Few-shot learning

no code implementations1 Jan 2021 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang

Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.

Few-Shot Learning

Cross-Lingual Dependency Parsing by POS-Guided Word Reordering

no code implementations Findings of the Association for Computational Linguistics 2020 Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang

The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).

Dependency Parsing Language Modelling +1

Attribute Propagation Network for Graph Zero-shot Learning

no code implementations24 Sep 2020 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.

Meta-Learning Zero-Shot Learning

Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference

no code implementations10 Sep 2020 Lu Liu, Zhenqiao Song, Xiaoqing Zheng

One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs.

Coreference Resolution

NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size

2 code implementations28 Aug 2020 Xuanyi Dong, Lu Liu, Katarzyna Musial, Bogdan Gabrys

In this paper, we propose NATS-Bench, a unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm.

Neural Architecture Search

Two-dimensional ferromagnetic semiconductor VBr3 with tunable anisotropy

no code implementations20 Aug 2020 Lu Liu, Ke Yang, Guangyu Wang, Hua Wu

Two-dimensional (2D) ferromagnets (FMs) have attracted widespread attention due to their prospects in spintronic applications.

Materials Science Strongly Correlated Electrons

Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

1 code implementation28 Jun 2020 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings.

Few-Shot Learning

Interpretable Time-series Classification on Few-shot Samples

1 code implementation3 Jun 2020 Wensi Tang, Lu Liu, Guodong Long

Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples.

Classification Few-Shot Learning +3

Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework

no code implementations15 Apr 2020 Jiehang Zeng, Lu Liu, Xiaoqing Zheng

A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.

General Classification Knowledge Graph Completion +4

Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

2 code implementations24 Feb 2020 Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang, Michael Blumenstein

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series.

General Classification Time Series +1

AutoML using Metadata Language Embeddings

2 code implementations8 Oct 2019 Iddo Drori, Lu Liu, Yi Nian, Sharath C. Koorathota, Jie S. Li, Antonio Khalil Moretti, Juliana Freire, Madeleine Udell

We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.

AutoML

Learning to Propagate for Graph Meta-Learning

1 code implementation NeurIPS 2019 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.

Few-Shot Image Classification

New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning

no code implementations19 Jul 2019 Shuqiang Lu, Lingyun Ying, Wenjie Lin, Yu Wang, Meining Nie, Kaiwen Shen, Lu Liu, Haixin Duan

With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area.

General Classification Intrusion Detection +1

Generating Responses with a Specific Emotion in Dialog

no code implementations ACL 2019 Zhenqiao Song, Xiaoqing Zheng, Lu Liu, Mu Xu, Xuanjing Huang

It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction.

MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy

no code implementations ICLR 2019 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.

Few-Shot Learning General Classification

Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning

no code implementations17 Jan 2019 Lu Liu, Robby T. Tan

Specifically, we propose two approaches, i. e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization.

From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation

no code implementations11 Jan 2019 Yan Zhao, Lu Liu, Chunhua Liu, Ruoyao Yang, Dong Yu

We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot.

Loss Guided Activation for Action Recognition in Still Images

no code implementations11 Dec 2018 Lu Liu, Robby T. Tan, ShaoDi You

This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features.

Action Recognition In Still Images

Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series

1 code implementation24 Oct 2016 Lu Liu, Zhiguang Wang

Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry especially in sensor and IoT related research and applications, but few advances has been achieved in effective time series visual analytics and interaction due to its temporal dimensionality and complex dynamics.

General Classification Time Series

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