Search Results for author: Jiaxin Shi

Found 35 papers, 24 papers with code

BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization

no code implementations10 Feb 2023 Weichao Zhao, Hezhen Hu, Wengang Zhou, Jiaxin Shi, Houqiang Li

In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model.

Pseudo Label Sign Language Recognition

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

no code implementations7 Dec 2022 Zhongwei Wan, Yichun Yin, Wei zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e. g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora.

General Knowledge Language Modelling +3

A Finite-Particle Convergence Rate for Stein Variational Gradient Descent

no code implementations17 Nov 2022 Jiaxin Shi, Lester Mackey

We provide a first finite-particle convergence rate for Stein variational gradient descent (SVGD).

Neural Eigenfunctions Are Structured Representation Learners

1 code implementation23 Oct 2022 Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu

In this paper, we introduce a scalable method for learning structured, adaptive-length deep representations.

Contrastive Learning Feature Importance +6

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation

1 code implementation24 May 2022 Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax.

Few-Shot Learning Semantic Parsing

HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval

no code implementations24 May 2022 Feilong Chen, Xiuyi Chen, Jiaxin Shi, Duzhen Zhang, Jianlong Chang, Qi Tian

It also achieves about +4. 9 AR on COCO and +3. 8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.

Cross-Modal Retrieval Retrieval +1

NeuralEF: Deconstructing Kernels by Deep Neural Networks

1 code implementation30 Apr 2022 Zhijie Deng, Jiaxin Shi, Jun Zhu

Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems.

Image Classification

Gradient Estimation with Discrete Stein Operators

1 code implementation19 Feb 2022 Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey

Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems.

Schema-Free Dependency Parsing via Sequence Generation

no code implementations28 Jan 2022 Boda Lin, Zijun Yao, Jiaxin Shi, Shulin Cao, Binghao Tang, Si Li, Yong Luo, Juanzi Li, Lei Hou

To remedy these drawbacks, we propose to achieve universal and schema-free Dependency Parsing (DP) via Sequence Generation (SG) DPSG by utilizing only the pre-trained language model (PLM) without any auxiliary structures or parsing algorithms.

Dependency Parsing Language Modelling

Double Control Variates for Gradient Estimation in Discrete Latent Variable Models

1 code implementation pproximateinference AABI Symposium 2022 Michalis K. Titsias, Jiaxin Shi

We introduce a variance reduction technique for score function estimators that makes use of double control variates.

Program Transfer for Answering Complex Questions over Knowledge Bases

1 code implementation ACL 2022 Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao

In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.

Program induction Semantic Parsing

TWAG: A Topic-Guided Wikipedia Abstract Generator

1 code implementation ACL 2021 Fangwei Zhu, Shangqing Tu, Jiaxin Shi, Juanzi Li, Lei Hou, Tong Cui

Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques.

Document Summarization Multi-Document Summarization

Sampling with Mirrored Stein Operators

1 code implementation ICLR 2022 Jiaxin Shi, Chang Liu, Lester Mackey

We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries.

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

2 code implementations10 Jun 2021 Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger Grosse

We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.

Gaussian Processes regression

TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

1 code implementation EMNLP 2021 Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, Hanwang Zhang

Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer.

Multi-hop Question Answering Question Answering

Neural Networks as Inter-Domain Inducing Points

no code implementations pproximateinference AABI Symposium 2021 Shengyang Sun, Jiaxin Shi, Roger Baker Grosse

Equivalences between infinite neural networks and Gaussian processes have been established for explaining the functional prior and training dynamics of deep learning models.

Gaussian Processes regression

Nonparametric Score Estimators

1 code implementation ICML 2020 Yuhao Zhou, Jiaxin Shi, Jun Zhu

Estimating the score, i. e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models that involve flexible yet intractable densities.

Unbiased Scene Graph Generation from Biased Training

6 code implementations CVPR 2020 Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, Hanwang Zhang

Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".

Causal Inference Graph Generation +1

Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model

1 code implementation IJCNLP 2019 Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.

Entity Alignment Graph Attention +1

Sparse Orthogonal Variational Inference for Gaussian Processes

1 code implementation pproximateinference AABI Symposium 2019 Jiaxin Shi, Michalis K. Titsias, andriy mnih

We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods.

Gaussian Processes Multi-class Classification +2

Scalable Training of Inference Networks for Gaussian-Process Models

2 code implementations27 May 2019 Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu

Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points.

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

6 code implementations17 May 2019 Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon

However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.

Variational Inference

Functional Variational Bayesian Neural Networks

2 code implementations ICLR 2019 Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse

We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defined directly on stochastic processes, i. e. distributions over functions.

Bayesian Inference Gaussian Processes +1

Explainable and Explicit Visual Reasoning over Scene Graphs

2 code implementations CVPR 2019 Jiaxin Shi, Hanwang Zhang, Juanzi Li

We aim to dismantle the prevalent black-box neural architectures used in complex visual reasoning tasks, into the proposed eXplainable and eXplicit Neural Modules (XNMs), which advance beyond existing neural module networks towards using scene graphs --- objects as nodes and the pairwise relationships as edges --- for explainable and explicit reasoning with structured knowledge.

Inductive Bias Visual Question Answering +1

Learning to Embed Sentences Using Attentive Recursive Trees

2 code implementations6 Nov 2018 Jiaxin Shi, Lei Hou, Juanzi Li, Zhiyuan Liu, Hanwang Zhang

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks.

Sentence Embedding Sentence-Embedding

Semi-crowdsourced Clustering with Deep Generative Models

1 code implementation NeurIPS 2018 Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang

We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.

Variational Inference

A Spectral Approach to Gradient Estimation for Implicit Distributions

3 code implementations ICML 2018 Jiaxin Shi, Shengyang Sun, Jun Zhu

Recently there have been increasing interests in learning and inference with implicit distributions (i. e., distributions without tractable densities).

Variational Inference

Message Passing Stein Variational Gradient Descent

no code implementations ICML 2018 Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.

Bayesian Inference Variational Inference

On Modeling Sense Relatedness in Multi-prototype Word Embedding

no code implementations IJCNLP 2017 Yixin Cao, Jiaxin Shi, Juanzi Li, Zhiyuan Liu, Chengjiang Li

To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model.

Language Modelling Named Entity Recognition (NER) +2

ZhuSuan: A Library for Bayesian Deep Learning

1 code implementation18 Sep 2017 Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.

Probabilistic Programming regression

Kernel Implicit Variational Inference

no code implementations ICLR 2018 Jiaxin Shi, Shengyang Sun, Jun Zhu

Recent progress in variational inference has paid much attention to the flexibility of variational posteriors.

General Classification regression +1

Towards Better Analysis of Deep Convolutional Neural Networks

no code implementations24 Apr 2016 Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification.

Image Classification

Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base

no code implementations3 Dec 2015 Jiaxin Shi, Jun Zhu

We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning process.

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