b'Yizhe Zhang'

research

∙ 09/09/2023

RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

Conformal prediction (CP) generates a set of predictions for a given tes...

0 Yizhe Zhang, et al. ∙

research

∙ 08/26/2023

SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

The Segment Anything Model (SAM) exhibits a capability to segment a wide...

0 Yizhe Zhang, et al. ∙

research

∙ 06/08/2023

BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping

Diffusion models have demonstrated excellent potential for generating di...

4 Jiatao Gu, et al. ∙

research

∙ 06/05/2023

PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model

Autoregressive models for text sometimes generate repetitive and low-qua...

2 Yizhe Zhang, et al. ∙

research

∙ 04/22/2023

Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model

The Segment Anything Model (SAM) is a recently developed large model for...

0 Yizhe Zhang, et al. ∙

research

∙ 03/11/2023

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

Training stability is of great importance to Transformers. In this work,...

5 Shuangfei Zhai, et al. ∙

research

∙ 03/02/2023

Interactive Text Generation

Users interact with text, image, code, or other editors on a daily basis...

5 Felix Faltings, et al. ∙

research

∙ 02/17/2023

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis

In this paper, we propose a novel approach (called GPT4MIA) that utilize...

0 Yizhe Zhang, et al. ∙

research

∙ 12/23/2022

EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)

The advance of computer-aided detection systems using deep learning open...

0 Haoran Wang, et al. ∙

research

∙ 10/25/2022

Bridging the Training-Inference Gap for Dense Phrase Retrieval

Building dense retrievers requires a series of standard procedures, incl...

0 Gyuwan Kim, et al. ∙

research

∙ 10/10/2022

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

Diffusion models (DMs) have recently emerged as SoTA tools for generativ...

7 Jiatao Gu, et al. ∙

research

∙ 09/04/2022

Data-Driven Deep Supervision for Skin Lesion Classification

Automatic classification of pigmented, non-pigmented, and depigmented no...

5 Suraj Mishra, et al. ∙

research

∙ 07/01/2022

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

We aim to quantitatively measure the practical usability of medical imag...

0 Yizhe Zhang, et al. ∙

research

∙ 06/02/2022

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

Deep learning (DL) based semantic segmentation methods have achieved exc...

12 Peixian Liang, et al. ∙

research

∙ 03/23/2022

Linearizing Transformer with Key-Value Memory Bank

Transformer has brought great success to a wide range of natural languag...

14 Yizhe Zhang, et al. ∙

research

∙ 03/18/2022

Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges

This is a report on the NSF Future Directions Workshop on Automatic Eval...

6 Shikib Mehri, et al. ∙

research

∙ 12/12/2021

Towards More Efficient Insertion Transformer with Fractional Positional Encoding

Auto-regressive neural sequence models have been shown to be effective a...

0 Zhisong Zhang, et al. ∙

research

∙ 11/03/2021

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

While deeply supervised networks are common in recent literature, they t...

0 Shubhankar Borse, et al. ∙

research

∙ 10/24/2021

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

In this paper, we propose a novel method, X-Distill, to improve the self...

0 Hong Cai, et al. ∙

research

∙ 10/24/2021

Perceptual Consistency in Video Segmentation

In this paper, we present a novel perceptual consistency perspective on ...

0 Yizhe Zhang, et al. ∙

research

∙ 10/24/2021

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

In video segmentation, generating temporally consistent results across f...

0 Yizhe Zhang, et al. ∙

research

∙ 06/14/2021

Automatic Document Sketching: Generating Drafts from Analogous Texts

The advent of large pre-trained language models has made it possible to ...

0 Zeqiu Wu, et al. ∙

research

∙ 05/14/2021

Joint Retrieval and Generation Training for Grounded Text Generation

Recent advances in large-scale pre-training such as GPT-3 allow seemingl...

2 Yizhe Zhang, et al. ∙

research

∙ 04/18/2021

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

Large pretrained generative models like GPT-3 often suffer from hallucin...

0 Tianyu Liu, et al. ∙

research

∙ 04/16/2021

An Adversarially-Learned Turing Test for Dialog Generation Models

The design of better automated dialogue evaluation metrics offers the po...

11 Xiang Gao, et al. ∙

research

∙ 04/06/2021

InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

We present a novel boundary-aware loss term for semantic segmentation us...

8 Shubhankar Borse, et al. ∙

research

∙ 03/24/2021

Finetuning Pretrained Transformers into RNNs

Transformers have outperformed recurrent neural networks (RNNs) in natur...

14 Jungo Kasai, et al. ∙

research

∙ 03/02/2021

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

The progress in Query-focused Multi-Document Summarization (QMDS) has be...

2 Ramakanth Pasunuru, et al. ∙

research

∙ 01/17/2021

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

GPT-3 has attracted lots of attention due to its superior performance ac...

0 Jiachang Liu, et al. ∙

research

∙ 01/02/2021

SDA: Improving Text Generation with Self Data Augmentation

Data augmentation has been widely used to improve deep neural networks i...

0 Ping Yu, et al. ∙

research

∙ 12/21/2020

Narrative Incoherence Detection

Motivated by the increasing popularity of intelligent editing assistant,...

13 Deng Cai, et al. ∙

research

∙ 12/17/2020

Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation

Automatic histopathology image segmentation is crucial to disease analys...

12 Hongxiao Wang, et al. ∙

research

∙ 10/12/2020

Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimat...

2 Guoyin Wang, et al. ∙

research

∙ 09/16/2020

Contextualized Perturbation for Textual Adversarial Attack

Adversarial examples expose the vulnerabilities of natural language proc...

0 Dianqi Li, et al. ∙

research

∙ 09/15/2020

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

Existing open-domain dialog models are generally trained to minimize the...

0 Xiang Gao, et al. ∙

research

∙ 08/14/2020

Weakly supervised cross-domain alignment with optimal transport

Cross-domain alignment between image objects and text sequences is key t...

9 Siyang Yuan, et al. ∙

research

∙ 08/06/2020

Structured Convolutions for Efficient Neural Network Design

In this work, we tackle model efficiency by exploiting redundancy in the...

68 Yash Bhalgat, et al. ∙

research

∙ 06/01/2020

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

Learning disentangled representations of natural language is essential f...

5 Pengyu Cheng, et al. ∙

research

∙ 05/01/2020

A Controllable Model of Grounded Response Generation

Current end-to-end neural conversation models inherently lack the flexib...

0 Zeqiu Wu, et al. ∙

research

∙ 05/01/2020

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

Large-scale pre-trained language models, such as BERT and GPT-2, have ac...

5 Yizhe Zhang, et al. ∙

research

∙ 04/30/2020

Contextual Text Style Transfer

We introduce a new task, Contextual Text Style Transfer - translating a ...

0 Yu Cheng, et al. ∙

research

∙ 04/05/2020

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

When trained effectively, the Variational Autoencoder (VAE) can be both ...

1 Chunyuan Li, et al. ∙

research

∙ 12/31/2019

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

Sequence generation models are commonly refined with reinforcement learn...

21 Xinjie Fan, et al. ∙

research

∙ 11/10/2019

INSET: Sentence Infilling with Inter-sentential Generative Pre-training

Missing sentence generation (or sentence infilling) fosters a wide range...

0 Yichen Huang, et al. ∙

research

∙ 11/08/2019

Unsupervised Common Question Generation from Multiple Documents using Reinforced Contrastive Coordinator

Web search engines today return a ranked list of document links in respo...

0 Woon Sang Cho, et al. ∙

research

∙ 11/01/2019

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

We present a large, tunable neural conversational response generation mo...

0 Yizhe Zhang, et al. ∙

research

∙ 10/25/2019

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

Ambiguous user queries in search engines result in the retrieval of docu...

0 Woon Sang Cho, et al. ∙

research

∙ 09/11/2019

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Recent unsupervised approaches to domain adaptation primarily focus on m...

0 Shuyang Dai, et al. ∙

research

∙ 09/03/2019

Structuring Latent Spaces for Stylized Response Generation

Generating responses in a targeted style is a useful yet challenging tas...

0 Xiang Gao, et al. ∙

research

∙ 08/31/2019

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

Recent efforts have shown promising results for person re-identification...

0 Bryan, et al. ∙

Yizhe Zhang

Featured Co-authors