no code implementations • EMNLP 2020 • Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
We propose a new methodology to assign and learn embeddings for numbers.
no code implementations • EMNLP (NLP4ConvAI) 2021 • Shuyang Dai, Guoyin Wang, Sunghyun Park, Sungjin Lee
In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.
1 code implementation • 25 Mar 2024 • Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, Yang Yang
We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs.
1 code implementation • 15 Mar 2024 • Xuemei Cao, Xin Yang, Shuyin Xia, Guoyin Wang, Tianrui Li
To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection.
1 code implementation • 24 Feb 2024 • Haiteng Zhao, Chang Ma, Guoyin Wang, Jing Su, Lingpeng Kong, Jingjing Xu, Zhi-Hong Deng, Hongxia Yang
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior.
no code implementations • 15 Feb 2024 • Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, Fei Wu
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM).
1 code implementation • 6 Feb 2024 • Rui Li, Jiwei Li, Jiawei Han, Guoyin Wang
Our research further underscores the significance of graph structure integration in LLM applications and identifies key factors for their success in node classification.
1 code implementation • 10 Jan 2024 • Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks.
1 code implementation • 31 Dec 2023 • Liang Wang, Dawei Dai, Shiyu Fu, Guoyin Wang
In specific scenarios, face sketch can be used to identify a person.
no code implementations • 9 Dec 2023 • Jiaxuan Liang, Jun Wang, Guoxian Yu, Shuyin Xia, Guoyin Wang
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.
1 code implementation • 9 Dec 2023 • Shuhe Wang, Beiming Cao, Shengyu Zhang, Xiaoya Li, Jiwei Li, Fei Wu, Guoyin Wang, Eduard Hovy
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely on unsupervised techniques or training signals that are only partially correlated with textual similarity, e. g., NLI-based datasets.
no code implementations • 3 Nov 2023 • Xiaofei Sun, Xiaoya Li, Shengyu Zhang, Shuhe Wang, Fei Wu, Jiwei Li, Tianwei Zhang, Guoyin Wang
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning.
no code implementations • 25 Oct 2023 • Yuan Li, Li Liu, Penggang Chen, Youmin Zhang, Guoyin Wang
Graph data widely exists in real life, with large amounts of data and complex structures.
1 code implementation • 12 Oct 2023 • Jinye Yang, Ji Xu, Di wu, Jianhang Tang, Shaobo Li, Guoyin Wang
The deviation of a classification model is caused by both class-wise and attribute-wise imbalance.
1 code implementation • 11 Oct 2023 • Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
1 code implementation • 26 Sep 2023 • Rui Li, Guoyin Wang, Jiwei Li
In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL.
1 code implementation • 21 Aug 2023 • Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, Guoyin Wang
This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs).
no code implementations • 16 Jun 2023 • Xiaofei Sun, Linfeng Dong, Xiaoya Li, Zhen Wan, Shuhe Wang, Tianwei Zhang, Jiwei Li, Fei Cheng, Lingjuan Lyu, Fei Wu, Guoyin Wang
In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
no code implementations • 29 May 2023 • Qin Xie, Qinghua Zhang, Shuyin Xia, Fan Zhao, Chengying Wu, Guoyin Wang, Weiping Ding
Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary.
no code implementations • 23 May 2023 • Minsik Oh, Jiwei Li, Guoyin Wang
We further introduce a novel analytic instrument of Semantic Compression method, for which we discover a correlation with uniformity and alignment.
1 code implementation • 15 May 2023 • Xiaofei Sun, Xiaoya Li, Jiwei Li, Fei Wu, Shangwei Guo, Tianwei Zhang, Guoyin Wang
This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e. g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning.
1 code implementation • 9 May 2023 • Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Tong Yu, Yufan Zhou, Guoyin Wang, Yiran Chen
This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.
no code implementations • 21 Apr 2023 • Shuyin Xia, Guoyin Wang, Xinbo Gao, Xiaoyu Lian
This mechanism inherently possesses an adaptive multi-granularity description capacity, resulting in computational traits such as efficiency, robustness, and interpretability.
1 code implementation • 20 Apr 2023 • Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang
GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e. g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract.
no code implementations • 18 Mar 2023 • Shuyin Xia, Jiancu Chen, Bin Hou, Guoyin Wang
The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.
no code implementations • 9 Mar 2023 • Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin
Open world classification is a task in natural language processing with key practical relevance and impact.
no code implementations • 7 Mar 2023 • Jiang Xie, Qiao Deng, Shuyin Xia, Yangzhou Zhao, Guoyin Wang, Xinbo Gao
In recent years, the problem of fuzzy clustering has been widely concerned.
no code implementations • 2 Mar 2023 • Jiang Xie, Shuyin Xia, Guoyin Wang, Xinbo Gao
We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST.
1 code implementation • 13 Feb 2023 • Minsik Oh, Joosung Lee, Jiwei Li, Guoyin Wang
Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation.
1 code implementation • 11 Feb 2023 • Dawei Dai, Yutang Li, Liang Wang, Shiyu Fu, Shuyin Xia, Guoyin Wang
In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig. 1).
no code implementations • 30 Dec 2022 • Jiang Xie, Pengfei Zhao, Shuyin Xia, Guoyin Wang, Dongdong Cheng
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis.
1 code implementation • 29 Dec 2022 • Li Liu, Penggang Chen, Xin Li, William K. Cheung, Youmin Zhang, Qun Liu, Guoyin Wang
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
1 code implementation • 5 Dec 2022 • Shuhe Wang, Yuxian Meng, Rongbin Ouyang, Jiwei Li, Tianwei Zhang, Lingjuan Lyu, Guoyin Wang
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.
no code implementations • 21 Oct 2022 • Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao, Yabin Shao
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing.
1 code implementation • 6 Oct 2022 • Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao, Jiancu Chen, Xiaoli Peng
Furthermore, a particle swarm optimization algorithm is designed to solve the dual model.
1 code implementation • 9 Sep 2022 • Yeon Seonwoo, Guoyin Wang, Changmin Seo, Sajal Choudhary, Jiwei Li, Xiang Li, Puyang Xu, Sunghyun Park, Alice Oh
In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence.
1 code implementation • 17 Aug 2022 • Ji Xu, Gang Ren, Yao Xiao, Shaobo Li, Guoyin Wang
Optimal leading forest (OLF) has been observed to have the advantage of revealing the difference evolution along a path within a subtree.
no code implementations • 28 Jul 2022 • Hongyu Shen, Jinoh Oh, Shuai Zhao, Guoyin Wang, Tara Taghavi, Sungjin Lee
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
no code implementations • 26 Jul 2022 • Ye Wang, Jingbo Liao, Hong Yu, Guoyin Wang, Xiaoxia Zhang, Li Liu
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
no code implementations • 29 May 2022 • Shuyin Xia, Jiang Xie, Guoyin Wang
Existing clustering methods are based on a single granularity of information, such as the distance and density of each data.
no code implementations • 7 May 2022 • Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Liyan Xu, Lawrence Carin
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed.
1 code implementation • 31 Mar 2022 • Shuhe Wang, Xiaoya Li, Yuxian Meng, Tianwei Zhang, Rongbin Ouyang, Jiwei Li, Guoyin Wang
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization, khandelwal2020nearest, meng2021gnn}, in this paper, we introduce a $k$ nearest neighbor NER ($k$NN-NER) framework, which augments the distribution of entity labels by assigning $k$ nearest neighbors retrieved from the training set.
no code implementations • 17 Mar 2022 • Dawei Dai, Chunjie Wang, Shuyin Xia, Yingge Liu, Guoyin Wang
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene.
no code implementations • 13 Jan 2022 • Dawei Dai, Xiaoyu Tang, Shuyin Xia, Yingge Liu, Guoyin Wang, Zizhong Chen
We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo.
no code implementations • 12 Jan 2022 • Shuyin Xia, Xiaochuan Dai, Guoyin Wang, Xinbo Gao, Elisabeth Giem
In addition, this paper first provides the mathematical models for the granular-ball covering.
no code implementations • 10 Jan 2022 • Shuyin Xia, Cheng Wang, Guoyin Wang, Weiping Ding, Xinbo Gao, JianHang Yu, Yujia Zhai, Zizhong Chen
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
no code implementations • 29 Dec 2021 • Shuyin Xia, Xinyu Bai, Guoyin Wang, Deyu Meng, Xinbo Gao, Zizhong Chen, Elisabeth Giem
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time.
no code implementations • 15 Dec 2021 • Shuhe Wang, Jiwei Li, Yuxian Meng, Rongbin Ouyang, Guoyin Wang, Xiaoya Li, Tianwei Zhang, Shi Zong
The core idea of Faster $k$NN-MT is to use a hierarchical clustering strategy to approximate the distance between the query and a data point in the datastore, which is decomposed into two parts: the distance between the query and the center of the cluster that the data point belongs to, and the distance between the data point and the cluster center.
1 code implementation • 22 Nov 2021 • Zihan Yan, Li Liu, Xin Li, William K. Cheung, Youmin Zhang, Qun Liu, Guoyin Wang
Social network alignment aims at aligning person identities across social networks.
no code implementations • 3 Nov 2021 • Dawei Dai, Yutang Li, Huanan Bao, Sy Xia, Guoyin Wang, Xiaoli Ma
From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.
no code implementations • 20 Oct 2021 • Xiaofei Sun, Diyi Yang, Xiaoya Li, Tianwei Zhang, Yuxian Meng, Han Qiu, Guoyin Wang, Eduard Hovy, Jiwei Li
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks.
no code implementations • 25 Sep 2021 • Joo-Kyung Kim, Guoyin Wang, Sungjin Lee, Young-Bum Kim
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.
1 code implementation • COLING 2022 • Nan Wang, Jiwei Li, Yuxian Meng, Xiaofei Sun, Han Qiu, Ziyao Wang, Guoyin Wang, Jun He
We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense.
Ranked #1 on Semantic Role Labeling on CoNLL 2005
1 code implementation • ACL 2021 • Xinnuo Xu, Guoyin Wang, Young-Bum Kim, Sungjin Lee
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language.
no code implementations • 3 Dec 2020 • Bo Liu, Ranglei Wu, Xiuli Bi, Bin Xiao, Weisheng Li, Guoyin Wang, Xinbo Gao
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Rui Wang, Shijing Si, Guoyin Wang, Lei Zhang, Lawrence Carin, Ricardo Henao
Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years.
no code implementations • 31 Oct 2020 • Shuyin Xia, Wenhua Li, Guoyin Wang, Xinbo Gao, Changqing Zhang, Elisabeth Giem
Based on the theorem, we propose the LRA framework for accelerating rough set algorithms.
no code implementations • EMNLP 2020 • Guoyin Wang, Chunyuan Li, Jianqiao Li, Hao Fu, Yuh-Chen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
no code implementations • 14 Aug 2020 • Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
1 code implementation • 22 Jun 2020 • Shijing Si, Rui Wang, Jedrek Wosik, Hao Zhang, David Dov, Guoyin Wang, Ricardo Henao, Lawrence Carin
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.
no code implementations • ACL 2020 • Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation.
no code implementations • 2 May 2020 • Shuyin Xia, Daowan Peng, Deyu Meng, Changqing Zhang, Guoyin Wang, Zizhong Chen, Wei Wei
The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.
1 code implementation • EMNLP 2020 • Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, Bill Dolan
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.
no code implementations • 20 Nov 2019 • Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.
no code implementations • 10 Nov 2019 • Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Shijing Si, Dinghan Shen, Dong Wang, Lawrence Carin
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks.
no code implementations • IJCNLP 2019 • Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, Lawrence Carin
Generating high-quality paraphrases is a fundamental yet challenging natural language processing task.
no code implementations • 20 Oct 2019 • Wenlin Wang, Hongteng Xu, Guoyin Wang, Wenqi Wang, Lawrence Carin
{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}
1 code implementation • NeurIPS 2019 • Kevin J Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.
1 code implementation • NeurIPS 2019 • Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
1 code implementation • 7 Jun 2019 • Guoyin Wang, Yan Song, Yue Zhang, Dong Yu
Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge.
no code implementations • ACL 2019 • Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
Constituting highly informative network embeddings is an important tool for network analysis.
no code implementations • NAACL 2019 • Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
We propose a topic-guided variational auto-encoder (TGVAE) model for text generation.
no code implementations • 30 May 2019 • Rui Wang, Guoyin Wang, Ricardo Henao
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset.
no code implementations • 17 Mar 2019 • Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
We propose a topic-guided variational autoencoder (TGVAE) model for text generation.
3 code implementations • 3 Jan 2019 • Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples.
no code implementations • ICLR 2019 • Kevin J Liang, Chunyuan Li, Guoyin Wang, Lawrence Carin
We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator.
no code implementations • 2 Nov 2018 • Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin
Sequence generation with reinforcement learning (RL) has received significant attention recently.
2 code implementations • ICML 2018 • Yunchen Pu, Shuyang Dai, Zhe Gan, Wei-Yao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
2 code implementations • ACL 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on Named Entity Recognition (NER) on CoNLL 2000
1 code implementation • ACL 2018 • Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Lawrence Carin, Ricardo Henao
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
2 code implementations • ACL 2018 • Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on Text Classification on DBpedia
no code implementations • ICLR 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
no code implementations • 25 Sep 2017 • Ji Xu, Guoyin Wang
We propose a sound assumption, arguing that: the neighboring data points are not in peer-to-peer relation, but in a partial-ordered relation induced by the local density and distance between the data; and the label of a center can be regarded as the contribution of its followers.
4 code implementations • NeurIPS 2017 • Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin
Learning latent representations from long text sequences is an important first step in many natural language processing applications.
no code implementations • Neurocomputing 2017 • Di wu, Mingsheng Shang, Xin Luo a, Ji Xu, Huyong Yan, Weihui Deng, Guoyin Wang
Having a multitude of unlabeled data and few labeled ones is a common problem in many practical ap- plications.
no code implementations • 22 Dec 2015 • Xi'ao Ma, Guoyin Wang, Hong Yu
This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model.
no code implementations • 12 Jun 2015 • Ji Xu, Guoyin Wang
There are two major advantages with the LT: One is dramatically reducing the running time of assigning noncenter data points to their cluster ID, because the assigning process is turned into just disconnecting the links from each center to its parent.