Search Results for author: Yizhe Zhang

Found 77 papers, 31 papers with code

What Makes Good In-Context Examples for GPT-3?

no code implementations DeeLIO (ACL) 2022 Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, Weizhu Chen

In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3’s in-context learning capabilities. Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt.

Natural Language Understanding Open-Domain Question Answering +1

Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models

1 code implementation1 Jul 2022 Yizhe Zhang, Suraj Mishra, Peixian Liang, Hao Zheng, Danny Z. Chen

We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.

Medical Image Segmentation Semantic Segmentation

H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation

no code implementations2 Jun 2022 Peixian Liang, Yizhe Zhang, Yifan Ding, Jianxu Chen, Chinedu S. Madukoma, Tim Weninger, Joshua D. Shrout, Danny Z. Chen

We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.

Instance Segmentation Semantic Segmentation

Linearizing Transformer with Key-Value Memory

no code implementations23 Mar 2022 Yizhe Zhang, Deng Cai

We demonstrate that MemSizer provides an improved balance between efficiency and accuracy over the vanilla transformer and other efficient transformer variants in three typical sequence generation tasks, including machine translation, abstractive text summarization, and language modeling.

Abstractive Text Summarization Language Modelling +2

Towards More Efficient Insertion Transformer with Fractional Positional Encoding

no code implementations12 Dec 2021 Zhisong Zhang, Yizhe Zhang, Bill Dolan

Nevertheless, due to the incompatibility between absolute positional encoding and insertion-based generation schemes, it needs to refresh the encoding of every token in the generated partial hypothesis at each step, which could be costly.

Text Generation

HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation

no code implementations3 Nov 2021 Shubhankar Borse, Hong Cai, Yizhe Zhang, Fatih Porikli

While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers.

Semantic Segmentation

X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation

no code implementations24 Oct 2021 Hong Cai, Janarbek Matai, Shubhankar Borse, Yizhe Zhang, Amin Ansari, Fatih Porikli

In order to enable such knowledge distillation across two different visual tasks, we introduce a small, trainable network that translates the predicted depth map to a semantic segmentation map, which can then be supervised by the teacher network.

Knowledge Distillation Monocular Depth Estimation +1

Perceptual Consistency in Video Segmentation

no code implementations24 Oct 2021 Yizhe Zhang, Shubhankar Borse, Hong Cai, Ying Wang, Ning Bi, Xiaoyun Jiang, Fatih Porikli

More specifically, by measuring the perceptual consistency between the predicted segmentation and the available ground truth on a nearby frame and combining it with the segmentation confidence, we can accurately assess the classification correctness on each pixel.

Semantic Segmentation Video Segmentation +1

AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation

1 code implementation24 Oct 2021 Yizhe Zhang, Shubhankar Borse, Hong Cai, Fatih Porikli

Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames.

Optical Flow Estimation Semantic Segmentation +2

Automatic Document Sketching: Generating Drafts from Analogous Texts

no code implementations Findings (ACL) 2021 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Bill Dolan

The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.

Text Generation

RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling

1 code implementation14 May 2021 Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.

Dialogue Generation Language Modelling

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

2 code implementations ACL 2022 Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, Bill Dolan

Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.

Text Generation

An Adversarially-Learned Turing Test for Dialog Generation Models

1 code implementation16 Apr 2021 Xiang Gao, Yizhe Zhang, Michel Galley, Bill Dolan

To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.

Dialogue Evaluation reinforcement-learning

InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

1 code implementation CVPR 2021 Shubhankar Borse, Ying Wang, Yizhe Zhang, Fatih Porikli

We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries.

Semantic Segmentation

Finetuning Pretrained Transformers into RNNs

1 code implementation EMNLP 2021 Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

Language Modelling Machine Translation +1

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

1 code implementation2 Mar 2021 Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley, Chenyan Xiong, Yizhe Zhang, Mohit Bansal, Jianfeng Gao

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.

Data Augmentation Document Summarization +1

What Makes Good In-Context Examples for GPT-$3$?

1 code implementation17 Jan 2021 Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, Weizhu Chen

Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.

Few-Shot Learning Natural Language Understanding +2

SDA: Improving Text Generation with Self Data Augmentation

no code implementations2 Jan 2021 Ping Yu, Ruiyi Zhang, Yang Zhao, Yizhe Zhang, Chunyuan Li, Changyou Chen

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.

Data Augmentation Imitation Learning +1

Towards Robust and Efficient Contrastive Textual Representation Learning

no code implementations1 Jan 2021 Liqun Chen, Yizhe Zhang, Dianqi Li, Chenyang Tao, Dong Wang, Lawrence Carin

There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.

Contrastive Learning Natural Language Processing +1

Narrative Incoherence Detection

no code implementations21 Dec 2020 Deng Cai, Yizhe Zhang, Yichen Huang, Wai Lam, Bill Dolan

We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.

Sentence Embedding

Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation

no code implementations17 Dec 2020 Hongxiao Wang, Hao Zheng, Jianxu Chen, Lin Yang, Yizhe Zhang, Danny Z. Chen

Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.

Image Generation Semantic Segmentation

Contextualized Perturbation for Textual Adversarial Attack

1 code implementation NAACL 2021 Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, Bill Dolan

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.

Adversarial Attack Language Modelling +1

Weakly supervised cross-domain alignment with optimal transport

no code implementations14 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.

Natural Language Processing

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

no code implementations ACL 2020 Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin

Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.

Conditional Text Generation Representation Learning +2

POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training

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.

Language Modelling Representation Learning +1

A Controllable Model of Grounded Response Generation

1 code implementation1 May 2020 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.

Informativeness Pretrained Language Models +1

Contextual Text Style Transfer

no code implementations Findings of the Association for Computational Linguistics 2020 Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar, Dianqi Li, Jingjing Liu

To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.

Style Transfer Text Style Transfer +1

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

1 code implementation EMNLP 2020 Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao

We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.

Language Modelling Representation Learning +1

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

1 code implementation ICLR 2020 Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou

To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.

Image Captioning Program Synthesis +1

INSET: Sentence Infilling with INter-SEntential Transformer

1 code implementation ACL 2020 Yichen Huang, Yizhe Zhang, Oussama Elachqar, Yu Cheng

Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion.

Natural Language Processing Natural Language Understanding +1

Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models

no code implementations WS 2019 Woon Sang Cho, Yizhe Zhang, Sudha Rao, Chris Brockett, Sungjin Lee

A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

no code implementations11 Sep 2019 Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu, Lawrence Carin

Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.

Unsupervised Domain Adaptation

Structuring Latent Spaces for Stylized Response Generation

1 code implementation IJCNLP 2019 Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.

Response Generation Style Transfer

Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking

no code implementations WS 2019 Xinnuo Xu, Yizhe Zhang, Lars Liden, Sungjin Lee

Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems.

Second-order Non-local Attention Networks for Person Re-identification

no code implementations31 Aug 2019 Bryan, Xia, Yuan Gong, Yizhe Zhang, Christian Poellabauer

Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts.

Person Re-Identification

Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

no code implementations7 Jun 2019 Yizhe Zhang, Michael T. C. Ying, Danny Z. Chen

Ablation study confirms the effectiveness of our proposed learning scheme for medical images.

Consistent Dialogue Generation with Self-supervised Feature Learning

1 code implementation13 Mar 2019 Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.

Dialogue Generation Response Generation

SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation

no code implementations28 Feb 2019 Yizhe Zhang, Lin Yang, Hao Zheng, Peixian Liang, Colleen Mangold, Raquel G. Loreto, David. P. Hughes, Danny Z. Chen

To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.

Data Augmentation Semantic Segmentation

Jointly Optimizing Diversity and Relevance in Neural Response Generation

no code implementations NAACL 2019 Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.

Dialogue Generation Response Generation

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

no code implementations ACL 2019 Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables.

Text Generation

Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation

no code implementations15 Jan 2019 Peixian Liang, Jianxu Chen, Hao Zheng, Lin Yang, Yizhe Zhang, Danny Z. Chen

The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.

Semantic Segmentation

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

1 code implementation10 Dec 2018 Hao Zheng, Yizhe Zhang, Lin Yang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen

In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.

3D Medical Imaging Segmentation Ensemble Learning +1

Hierarchically-Structured Variational Autoencoders for Long Text Generation

no code implementations27 Sep 2018 Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Lawrence Carin

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation.

Text Generation

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

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).

Joint Embedding of Words and Labels for Text Classification

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.

Classification General Classification +2

A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs

no code implementations1 Feb 2018 Peixian Liang, Jianxu Chen, Pavel A. Brodskiy, Qinfeng Wu, Yejia Zhang, Yizhe Zhang, Lin Yang, Jeremiah J. Zartman, Danny Z. Chen

A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.

On the Use of Word Embeddings Alone to Represent Natural Language Sequences

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.

Word Embeddings

Deconvolutional Latent-Variable Model for Text Sequence Matching

no code implementations21 Sep 2017 Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.

Text Matching

Triangle Generative Adversarial Networks

1 code implementation NeurIPS 2017 Zhe Gan, Liqun Chen, Wei-Yao Wang, Yunchen Pu, Yizhe Zhang, Hao liu, Chunyuan Li, Lawrence Carin

The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.

Image-to-Image Translation Semi-Supervised Image Classification +1

A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC

no code implementations4 Sep 2017 Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin

However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.

Stochastic Optimization

Deconvolutional Paragraph Representation Learning

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.

General Classification Natural Language Processing +3

Stochastic Gradient Monomial Gamma Sampler

no code implementations ICML 2017 Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin

A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo.

Stochastic Gradient MCMC with Stale Gradients

no code implementations NeurIPS 2016 Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin

In this paper we develop theory to show that while the bias and MSE of an SG-MCMC algorithm depend on the staleness of stochastic gradients, its estimation variance (relative to the expected estimate, based on a prescribed number of samples) is independent of it.

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

no code implementations NeurIPS 2016 Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.

Learning a Hybrid Architecture for Sequence Regression and Annotation

no code implementations16 Dec 2015 Yizhe Zhang, Ricardo Henao, Lawrence Carin, Jianling Zhong, Alexander J. Hartemink

When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states.

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