Search Results for author: Jian Yin

Found 65 papers, 23 papers with code

Analytical Reasoning of Text

1 code implementation Findings (NAACL) 2022 Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.

Cross Initialization for Personalized Text-to-Image Generation

1 code implementation26 Dec 2023 Lianyu Pang, Jian Yin, Haoran Xie, Qiping Wang, Qing Li, Xudong Mao

Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability.

Improving Entropy-Based Test-Time Adaptation from a Clustering View

no code implementations31 Oct 2023 Guoliang Lin, Hanjiang Lai, Yan Pan, Jian Yin

In this paper, we introduce a new perspective on the EBTTA, which interprets these methods from a view of clustering.

Clustering Test

LEGO-Prover: Neural Theorem Proving with Growing Libraries

1 code implementation1 Oct 2023 Haiming Wang, Huajian Xin, Chuanyang Zheng, Lin Li, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, Jian Yin, Zhenguo Li, Heng Liao, Xiaodan Liang

Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.

 Ranked #1 on Automated Theorem Proving on miniF2F-test (Pass@100 metric)

Automated Theorem Proving

PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs

1 code implementation ICCV 2023 Wentao Hu, Jia Zheng, Zixin Zhang, Xiaojun Yuan, Jian Yin, Zihan Zhou

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models.

3D Reconstruction

LongCoder: A Long-Range Pre-trained Language Model for Code Completion

1 code implementation26 Jun 2023 Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian McAuley

In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task.

Code Completion Language Modelling

Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning

no code implementations20 Jun 2023 Huiguo He, Tianfu Wang, Huan Yang, Jianlong Fu, Nicholas Jing Yuan, Jian Yin, Hongyang Chao, Qi Zhang

The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design.

Attribute Image Generation +3

UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning

no code implementations1 Jun 2023 Xiao Dong, Runhui Huang, XiaoYong Wei, Zequn Jie, Jianxing Yu, Jian Yin, Xiaodan Liang

Recent advances in vision-language pre-training have enabled machines to perform better in multimodal object discrimination (e. g., image-text semantic alignment) and image synthesis (e. g., text-to-image generation).

Contrastive Learning Retrieval

Selective Pre-training for Private Fine-tuning

1 code implementation23 May 2023 Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang

Besides performance improvements, our framework also shows that with careful pre-training and private fine-tuning, smaller models can match the performance of much larger models that do not have access to private data, highlighting the promise of private learning as a tool for model compression and efficiency.

Model Compression Transfer Learning

Deep Hashing With Minimal-Distance-Separated Hash Centers

no code implementations CVPR 2023 Liangdao Wang, Yan Pan, Cong Liu, Hanjiang Lai, Jian Yin, Ye Liu

This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem.

Deep Hashing Image Retrieval +1

Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers

no code implementations20 Oct 2022 Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan

This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.

Decision Making

Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers

no code implementations12 Oct 2022 Ge-Yang Ke, Yan Pan, Jian Yin, Chang-Qin Huang

The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: (1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks; (2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task.

Multi-Task Learning

Improving Task Generalization via Unified Schema Prompt

no code implementations5 Aug 2022 Wanjun Zhong, Yifan Gao, Ning Ding, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

Task generalization has been a long standing challenge in Natural Language Processing (NLP).

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent

1 code implementation6 Jun 2022 Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang

Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning.

ProQA: Structural Prompt-based Pre-training for Unified Question Answering

1 code implementation NAACL 2022 Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.

Continual Learning Few-Shot Learning +2

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

2 code implementations ACL 2022 Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin

Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.

Code Completion Code Search +1

Reasoning over Hybrid Chain for Table-and-Text Open Domain QA

no code implementations15 Jan 2022 Wanjun Zhong, JunJie Huang, Qian Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.

Open-Domain Question Answering

ViT2Hash: Unsupervised Information-Preserving Hashing

no code implementations14 Jan 2022 Qinkang Gong, Liangdao Wang, Hanjiang Lai, Yan Pan, Jian Yin

Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data.

Quantization

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

1 code implementation12 Dec 2021 Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.

Graph Embedding Node Classification

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

2 code implementations COLING 2022 Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.

Contrastive Learning Knowledge Graph Embedding +7

Availability Attacks Create Shortcuts

1 code implementation1 Nov 2021 Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

We are the first to unveil an important population property of the perturbations of these attacks: they are almost \textbf{linearly separable} when assigned with the target labels of the corresponding samples, which hence can work as \emph{shortcuts} for the learning objective.

Data Poisoning

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

2 code implementations26 Oct 2021 Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu

To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

no code implementations14 Oct 2021 Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.

Drug Discovery Molecular Docking +1

Learning mixture of neural temporal point processes for event sequence clustering

no code implementations29 Sep 2021 Yunhao Zhang, Junchi Yan, Zhenyu Ren, Jian Yin

To fill the gap, we propose Mixture of Neural Temporal Point Processes (NTPP-MIX), a general framework that can utilize many existing NTPPs for event sequence clustering.

Clustering Point Processes

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

1 code implementation14 Sep 2021 Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.

Multivariate Time Series Forecasting Time Series

Learning to Complete Code with Sketches

no code implementations ICLR 2022 Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis

To evaluate models, we consider both ROUGE as well as a new metric RegexAcc that measures success of generating completions matching long outputs with as few holes as possible.

Code Completion Code Generation +1

Large Scale Private Learning via Low-rank Reparametrization

1 code implementation17 Jun 2021 Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence.

AR-LSAT: Investigating Analytical Reasoning of Text

1 code implementation14 Apr 2021 Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.

REST: Relational Event-driven Stock Trend Forecasting

no code implementations15 Feb 2021 Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu

To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.

Neural Deepfake Detection with Factual Structure of Text

1 code implementation EMNLP 2020 Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.

DeepFake Detection Face Swapping +1

GraphCodeBERT: Pre-training Code Representations with Data Flow

1 code implementation ICLR 2021 Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.

Clone Detection Code Completion +7

How Does Data Augmentation Affect Privacy in Machine Learning?

1 code implementation21 Jul 2020 Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation.

BIG-bench Machine Learning Data Augmentation

Low-Resource Generation of Multi-hop Reasoning Questions

no code implementations ACL 2020 Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.

Machine Reading Comprehension valid

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder

1 code implementation ACL 2020 Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs.

Common Sense Reasoning Text Generation

SEEK: Segmented Embedding of Knowledge Graphs

1 code implementation ACL 2020 Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.

Knowledge Graph Embedding Knowledge Graphs +2

Controllable Face Aging

no code implementations20 Dec 2019 Haien Zeng, Hanjiang Lai, Jian Yin

Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e. g., race).

Attribute Disentanglement +1

Gradient Perturbation is Underrated for Differentially Private Convex Optimization

no code implementations26 Nov 2019 Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu, Jian Yin

By using the \emph{expected curvature}, we show that gradient perturbation can achieve a significantly improved utility guarantee that can theoretically justify the advantage of gradient perturbation over other perturbation methods.

Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval

no code implementations19 Nov 2019 Haien Zeng, Hanjiang Lai, Jian Yin

Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation.

Code Generation Deep Hashing +1

Modal-aware Features for Multimodal Hashing

no code implementations19 Nov 2019 Haien Zeng, Hanjiang Lai, Hanlu Chu, Yong Tang, Jian Yin

The modal-aware operation consists of a kernel network and an attention network.

Retrieval

Reasoning Over Semantic-Level Graph for Fact Checking

no code implementations ACL 2020 Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.

Claim Verification Fact Checking +4

Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

no code implementations ACL 2019 Jianxing Yu, Zheng-Jun Zha, Jian Yin

This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills.

Reading Comprehension

Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing

no code implementations ACL 2019 Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment.

Meta-Learning Retrieval +1

Fashion Editing with Adversarial Parsing Learning

no code implementations CVPR 2020 Haoye Dong, Xiaodan Liang, Yixuan Zhang, Xujie Zhang, Zhenyu Xie, Bowen Wu, Ziqi Zhang, Xiaohui Shen, Jian Yin

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.

Generative Adversarial Network Human Parsing +1

Feature Pyramid Hashing

no code implementations4 Apr 2019 Yifan Yang, Libing Geng, Hanjiang Lai, Yan Pan, Jian Yin

In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval.

Deep Hashing Image Retrieval

Deep Policy Hashing Network with Listwise Supervision

no code implementations3 Apr 2019 Shaoying Wang, Haijiang Lai, Yifan Yang, Jian Yin

The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain a whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e. g., mean average precision (MAP), and 3) the database network is updated as the query network.

Deep Hashing Image Retrieval

Towards Multi-pose Guided Virtual Try-on Network

no code implementations ICCV 2019 Haoye Dong, Xiaodan Liang, Bochao Wang, Hanjiang Lai, Jia Zhu, Jian Yin

Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.

Fashion Synthesis Generative Adversarial Network +3

Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

no code implementations NeurIPS 2018 Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.

Generative Adversarial Network Image Generation

Improving Question Answering by Commonsense-Based Pre-Training

no code implementations5 Sep 2018 Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge.

Question Answering

Neural Math Word Problem Solver with Reinforcement Learning

no code implementations COLING 2018 Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin

Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.

Feature Engineering Math +4

Using Intermediate Representations to Solve Math Word Problems

no code implementations ACL 2018 Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.

Math Math Word Problem Solving

HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

no code implementations26 Nov 2017 Xi Zhang, Siyu Zhou, Jiashi Feng, Hanjiang Lai, Bo Li, Yan Pan, Jian Yin, Shuicheng Yan

The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.

Cross-Modal Retrieval Retrieval

Personalized and Occupational-aware Age Progression by Generative Adversarial Networks

no code implementations26 Nov 2017 Siyu Zhou, Weiqiang Zhao, Jiashi Feng, Hanjiang Lai, Yan Pan, Jian Yin, Shuicheng Yan

Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.

Human Aging

Learning Fine-Grained Expressions to Solve Math Word Problems

no code implementations EMNLP 2017 Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin

This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.

Math Math Word Problem Solving

Deep Recurrent Regression for Facial Landmark Detection

no code implementations30 Oct 2015 Hanjiang Lai, Shengtao Xiao, Yan Pan, Zhen Cui, Jiashi Feng, Chunyan Xu, Jian Yin, Shuicheng Yan

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.

Facial Landmark Detection regression

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