Search Results for author: Yu Sun

Found 140 papers, 52 papers with code

Test-Time Training for Generalization under Distribution Shifts

no code implementations ICML 2020 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, University of California Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when training and test data come from different distributions.

Image Classification Self-Supervised Learning

Tool-Augmented Reward Modeling

no code implementations2 Oct 2023 Lei LI, Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Ningyu Zhang, Hua Wu

Our study delves into the integration of external tools into RMs, enabling them to interact with diverse external sources and construct task-specific tool engagement and reasoning traces in an autoregressive manner.

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

no code implementations18 Jul 2023 Hongwei Zheng, Han Li, Bowen Shi, Wenrui Dai, Botao Wan, Yu Sun, Min Guo, Hongkai Xiong

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence.

3D Human Pose Estimation

Test-Time Training on Video Streams

no code implementations11 Jul 2023 Renhao Wang, Yu Sun, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang

Before making a prediction on each test instance, the model is trained on the same instance using a self-supervised task, such as image reconstruction with masked autoencoders.

Image Reconstruction Panoptic Segmentation

TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments

3 code implementations CVPR 2023 Yu Sun, Qian Bao, Wu Liu, Tao Mei, Michael J. Black

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current methods still cannot reliably estimate moving humans in global coordinates, which is critical for many applications.

3D Human Pose Estimation regression

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials

2 code implementations31 May 2023 Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang, Weibin Li, Yu Sun, dianhai yu

These spatial-based HGNNs neglect the utilization of spectral graph convolutions, which are the foundation of Graph Convolutional Networks (GCN) on homogeneous graphs.

Graph Learning Node Classification +1

Test-Time Training on Nearest Neighbors for Large Language Models

1 code implementation29 May 2023 Moritz Hardt, Yu Sun

Many recent efforts aim to augment language models with relevant information retrieved from a database at test time.

Language Modelling Prompt Engineering

Learning Task-Specific Strategies for Accelerated MRI

no code implementations25 Apr 2023 Zihui Wu, Tianwei Yin, Yu Sun, Robert Frost, Andre van der Kouwe, Adrian V. Dalca, Katherine L. Bouman

Leveraging recent co-design techniques, TACKLE jointly optimizes subsampling, reconstruction, and prediction strategies to enhance the performance on the downstream task.

Image Reconstruction

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

no code implementations15 Feb 2023 Han Li, Bowen Shi, Wenrui Dai, Hongwei Zheng, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies.

3D Human Pose Estimation

ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models

no code implementations9 Feb 2023 Pengfei Zhu, Chao Pang, Yekun Chai, Lei LI, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu

In response to this lacuna, this paper introduces a pioneering contribution in the form of a text-to-waveform music generation model, underpinned by the utilization of diffusion models.

Music Generation Text-to-Music Generation

Instance-wise Batch Label Restoration via Gradients in Federated Learning

1 code implementation International Conference on Learning Representations 2023 Kailang Ma, Yu Sun, Jian Cui, Dawei Li, Zhenyu Guan and Jianwei Liu

Furthermore, we demonstrate that our method facilitates the existing gradient inversion attacks by exploiting the recovered labels, with an increase of 6-7 in PSNR on both MNIST and CIFAR100.

Federated Learning

ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization

1 code implementation9 Jan 2023 Weixin Liu, Xuyi Chen, Jiaxiang Liu, Shikun Feng, Yu Sun, Hao Tian, Hua Wu

Experimental results demonstrate that our method yields a student with much better generalization, significantly outperforms existing baselines, and establishes a new state-of-the-art result on in-domain, out-domain, and low-resource datasets in the setting of task-agnostic distillation.

Knowledge Distillation Language Modelling +1

ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages

1 code implementation13 Dec 2022 Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu

Extensive results show that ERNIE-Code outperforms previous multilingual LLMs for PL or NL across a wide range of end tasks of code intelligence, including multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation.

Code Summarization Language Modelling +2

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

no code implementations21 Nov 2022 Paul Goyes, Edwin Vargas, Claudia Correa, Yu Sun, Ulugbek Kamilov, Brendt Wohlberg, Henry Arguello

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging.

ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation

no code implementations9 Nov 2022 Bin Shan, Yaqian Han, Weichong Yin, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance.

Contrastive Learning Language Modelling +4

ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech

2 code implementations7 Nov 2022 Xiaoran Fan, Chao Pang, Tian Yuan, He Bai, Renjie Zheng, Pengfei Zhu, Shuohuan Wang, Junkun Chen, Zeyu Chen, Liang Huang, Yu Sun, Hua Wu

In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing.

Representation Learning Speech Synthesis +2

SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking

no code implementations31 Oct 2022 Xiaotian Zhang, Hang Yan, Yu Sun, Xipeng Qiu

To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method.

Chinese Spell Checking Contrastive Learning

Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards

no code implementations21 Oct 2022 Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts.

CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval

no code implementations18 Oct 2022 Wenbiao Li, Pan Tang, Zhengfan Wu, Weixue Lu, Minghua Zhang, Zhenlei Tian, Daiting Shi, Yu Sun, Simiu Gu, Dawei Yin

Meanwhile, we introduce sentence-level semantic interaction to design a multi-embedding-based retrieval (MEBR) model, which can generate multiple embeddings to deal with different potential queries by using frequently clicked sentences in web pages.


ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training

1 code implementation30 Sep 2022 Bin Shan, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.

Contrastive Learning Cross-Modal Retrieval +4

ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding

no code implementations18 Sep 2022 Wenjin Wang, Zhengjie Huang, Bin Luo, Qianglong Chen, Qiming Peng, Yinxu Pan, Weichong Yin, Shikun Feng, Yu Sun, dianhai yu, Yin Zhang

At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method.

Common Sense Reasoning document understanding +1

Test-Time Training with Masked Autoencoders

1 code implementation15 Sep 2022 Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros

Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision.

WOC: A Handy Webcam-based 3D Online Chatroom

no code implementations2 Sep 2022 Chuanhang Yan, Yu Sun, Qian Bao, Jinhui Pang, Wu Liu, Tao Mei

We develop WOC, a webcam-based 3D virtual online chatroom for multi-person interaction, which captures the 3D motion of users and drives their individual 3D virtual avatars in real-time.

A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks

no code implementations29 May 2022 Binyan Hu, Yu Sun, A. K. Qin

Combining multiple DA methods, namely multi-DA, for DNN training, provides a way to boost generalisation.

Data Augmentation Image Classification +2

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

1 code implementation19 May 2022 Yang Xiang, Zhihua Wu, Weibao Gong, Siyu Ding, Xianjie Mo, Yuang Liu, Shuohuan Wang, Peng Liu, Yongshuai Hou, Long Li, Bin Wang, Shaohuai Shi, Yaqian Han, Yue Yu, Ge Li, Yu Sun, Yanjun Ma, dianhai yu

We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.

Cross-Lingual Natural Language Inference Distributed Computing +2

Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning

1 code implementation13 May 2022 Huijuan Wang, Siming Dai, Weiyue Su, Hui Zhong, Zeyang Fang, Zhengjie Huang, Shikun Feng, Zeyu Chen, Yu Sun, dianhai yu

Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.

Knowledge Graphs Representation Learning

Robust Neonatal Face Detection in Real-world Clinical Settings

no code implementations1 Apr 2022 Jacqueline Hausmann, Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Yu Sun

Current face detection algorithms are extremely generalized and can obtain decent accuracy when detecting the adult faces.

Face Detection

ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention

no code implementations23 Mar 2022 Yang Liu, Jiaxiang Liu, Li Chen, Yuxiang Lu, Shikun Feng, Zhida Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer.

Sparse Learning text-classification +1

Learning Cross-Video Neural Representations for High-Quality Frame Interpolation

1 code implementation28 Feb 2022 Wentao Shangguan, Yu Sun, Weijie Gan, Ulugbek S. Kamilov

This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors.

Video Frame Interpolation Vocal Bursts Intensity Prediction

Graph Neural Networks for Double-Strand DNA Breaks Prediction

no code implementations4 Jan 2022 Xu Wang, Huan Zhao, WeiWei Tu, Hao Li, Yu Sun, Xiaochen Bo

Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause abnormal chromosomal rearrangements.

ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation

2 code implementations31 Dec 2021 Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

To explore the landscape of large-scale pre-training for bidirectional text-image generation, we train a 10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million (Chinese) image-text pairs which achieves state-of-the-art performance for both text-to-image and image-to-text tasks, obtaining an FID of 7. 9 on MS-COCO for text-to-image synthesis and best results on COCO-CN and AIC-ICC for image captioning.

Image Captioning Quantization +1

Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems

no code implementations4 Dec 2021 Md. Sadman Sakib, David Paulius, Yu Sun

To address the problem of producing novel and flexible task plans called task trees, we explore how we can derive plans with concepts not originally in the robot's knowledge base.

Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems

1 code implementation2 Dec 2021 Weibin Li, Mingkai He, Zhengjie Huang, Xianming Wang, Shikun Feng, Weiyue Su, Yu Sun

In recent years, owing to the outstanding performance in graph representation learning, graph neural network (GNN) techniques have gained considerable interests in many real-world scenarios, such as recommender systems and social networks.

graph construction Graph Representation Learning +1

Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp

no code implementations30 Nov 2021 Tianze Chen, Adheesh Shenoy, Anzhelika Kolinko, Syed Shah, Yu Sun

To do so, a robot needs to grasp within a pile, sense the number of objects in the grasp before lifting, and predict the number of objects that will remain in the grasp after lifting.

Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields

1 code implementation27 Nov 2021 Renhao Liu, Yu Sun, Jiabei Zhu, Lei Tian, Ulugbek Kamilov

The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model, without any ground-truth RI maps.

Zero-Shot Learning

Hierarchical Graph Networks for 3D Human Pose Estimation

1 code implementation23 Nov 2021 Han Li, Bowen Shi, Wenrui Dai, Yabo Chen, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong

Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.

3D Human Pose Estimation

ERNIE-SPARSE: Robust Efficient Transformer Through Hierarchically Unifying Isolated Information

no code implementations29 Sep 2021 Yang Liu, Jiaxiang Liu, Yuxiang Lu, Shikun Feng, Yu Sun, Zhida Feng, Li Chen, Hao Tian, Hua Wu, Haifeng Wang

The first factor is information bottleneck sensitivity, which is caused by the key feature of Sparse Transformer — only a small number of global tokens can attend to all other tokens.

text-classification Text Classification

Pattern Recognition in Vital Signs Using Spectrograms

no code implementations5 Aug 2021 Sidharth Srivatsav Sribhashyam, Md Sirajus Salekin, Dmitry Goldgof, Ghada Zamzmi, Mark Last, Yu Sun

The results from the proposed approach are promising with an accuracy of 91. 55% and 91. 67% in prediction and classification tasks respectively.

Time Series Time Series Analysis

Research Challenges and Progress in Robotic Grasping and Manipulation Competitions

no code implementations3 Aug 2021 Yu Sun, Joe Falco, Maximo A. Roa, Berk Calli

This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs).

Robotic Grasping

Alpha at SemEval-2021 Task 6: Transformer Based Propaganda Classification

no code implementations SEMEVAL 2021 Zhida Feng, Jiji Tang, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen

This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes.


Deformation-Compensated Learning for Image Reconstruction without Ground Truth

1 code implementation12 Jul 2021 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.

Image Reconstruction

ERNIE-Tiny : A Progressive Distillation Framework for Pretrained Transformer Compression

1 code implementation4 Jun 2021 Weiyue Su, Xuyi Chen, Shikun Feng, Jiaxiang Liu, Weixin Liu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Specifically, the first stage, General Distillation, performs distillation with guidance from pretrained teacher, gerenal data and latent distillation loss.

Knowledge Distillation

Parallel sentences mining with transfer learning in an unsupervised setting

no code implementations NAACL 2021 Yu Sun, Shaolin Zhu, Feng Yifan, Chenggang Mi

In this paper, we propose an approach based on transfer learning to mine parallel sentences in the unsupervised setting. With the help of bilingual corpora of rich-resource language pairs, we can mine parallel sentences without bilingual supervision of low-resource language pairs.

Machine Translation NMT +2

A Road-map to Robot Task Execution with the Functional Object-Oriented Network

no code implementations1 Jun 2021 David Paulius, Alejandro Agostini, Yu Sun, Dongheui Lee

Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots.

Evaluating Recipes Generated from Functional Object-Oriented Network

no code implementations1 Jun 2021 Md Sadman Sakib, Hailey Baez, David Paulius, Yu Sun

We first automatically convert task trees to recipes, and we then compare them with the human-created recipes in the Recipe1M+ dataset via a survey.


Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective

no code implementations23 Apr 2021 Wu Liu, Qian Bao, Yu Sun, Tao Mei

We believe this survey will provide the readers with a deep and insightful understanding of monocular human pose estimation.

3D Human Pose Estimation

CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems

1 code implementation9 Feb 2021 Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.

Image Reconstruction

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

1 code implementation22 Jan 2021 Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov

Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.

ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora

2 code implementations EMNLP 2021 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

In this paper, we propose ERNIE-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance.


Developing Motion Code Embedding for Action Recognition in Videos

no code implementations10 Dec 2020 Maxat Alibayev, David Paulius, Yu Sun

In this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes.

Action Recognition In Videos

Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment

no code implementations3 Dec 2020 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration.

Joint Reconstruction and Calibration using Regularization by Denoising

no code implementations26 Nov 2020 Mingyang Xie, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.

Denoising Image Reconstruction

Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization

no code implementations19 Nov 2020 Yongqiang Huang, Juan Wilches, Yu Sun

We have also evaluated the proposed self-supervised generalization approach using unaccustomed containers that are far different from the ones in the training set.

Self-Supervised Learning

Synthetic Training for Monocular Human Mesh Recovery

no code implementations27 Oct 2020 Yu Sun, Qian Bao, Wu Liu, Wenpeng Gao, Yili Fu, Chuang Gan, Tao Mei

To solve this problem, we design a multi-branch framework to disentangle the regression of different body properties, enabling us to separate each component's training in a synthetic training manner using unpaired data available.

Human Mesh Recovery

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

no code implementations ICLR 2021 Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek S. Kamilov

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.


Deep Image Reconstruction using Unregistered Measurements without Groundtruth

no code implementations29 Sep 2020 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.

Image Reconstruction

Weakly-Supervised Online Hashing

no code implementations16 Sep 2020 Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu

However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i. e., social images are usually generated periodically or collected in a stream fashion.

Image Retrieval Retrieval

Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

3 code implementations8 Sep 2020 Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, Yu Sun

Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.

General Classification Node Classification +1

ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model

no code implementations SEMEVAL 2020 Zhengjie Huang, Shikun Feng, Weiyue Su, Xuyi Chen, Shuohuan Wang, Jiaxiang Liu, Xuan Ouyang, Yu Sun

This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media.

Data Augmentation Feature Engineering +2

Monocular, One-stage, Regression of Multiple 3D People

1 code implementation ICCV 2021 Yu Sun, Qian Bao, Wu Liu, Yili Fu, Michael J. Black, Tao Mei

Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map.

 Ranked #1 on 3D Multi-Person Mesh Recovery on Relative Human (using extra training data)

3D Depth Estimation 3D Multi-Person Mesh Recovery +2

Estimating Motion Codes from Demonstration Videos

no code implementations31 Jul 2020 Maxat Alibayev, David Paulius, Yu Sun

A motion taxonomy can encode manipulations as a binary-encoded representation, which we refer to as motion codes.

A Motion Taxonomy for Manipulation Embedding

no code implementations13 Jul 2020 David Paulius, Nicholas Eales, Yu Sun

To represent motions from a mechanical point of view, this paper explores motion embedding using the motion taxonomy.

Self-Supervised Policy Adaptation during Deployment

2 code implementations ICLR 2021 Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang

A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.

Scalable Plug-and-Play ADMM with Convergence Guarantees

no code implementations5 Jun 2020 Yu Sun, Zihui Wu, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.

Provable Convergence of Plug-and-Play Priors with MMSE denoisers

no code implementations15 May 2020 Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.

Compressive Sensing Image Reconstruction

Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation

no code implementations ACL 2020 Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang

This approach requires complete state-action annotations of human-to-human dialogues (i. e., expert demonstrations), which is labor intensive.

Task-Oriented Dialogue Systems

Learning Individual Models for Imputation (Technical Report)

no code implementations7 Apr 2020 Aoqian Zhang, Shaoxu Song, Yu Sun, Jian-Min Wang

We propose to adaptively learn individual models over various number l of neighbors for different complete tuples.

Imputation regression

First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

no code implementations24 Mar 2020 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain.

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

4 code implementations26 Jan 2020 Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.

 Ranked #1 on Question Generation on SQuAD1.1 (using extra training data)

Abstractive Text Summarization Dialogue Generation +3

MALA: Cross-Domain Dialogue Generation with Action Learning

no code implementations18 Dec 2019 Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang

These two components, however, have a discrepancy in their objectives, i. e., task completion and language quality.

Dialogue Generation Response Generation +2

Manipulation Motion Taxonomy and Coding for Robots

no code implementations1 Oct 2019 David Paulius, Yongqiang Huang, Jason Meloncon, Yu Sun

This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations.

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

3 code implementations29 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

Building change detection for remote sensing images CARLA MAP Leaderboard +4

Unsupervised Domain Adaptation through Self-Supervision

3 code implementations26 Sep 2019 Yu Sun, Eric Tzeng, Trevor Darrell, Alexei A. Efros

This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.

Unsupervised Domain Adaptation

Test-Time Training for Out-of-Distribution Generalization

no code implementations25 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions.

Image Classification Out-of-Distribution Generalization +1

Infusing Learned Priors into Model-Based Multispectral Imaging

no code implementations20 Sep 2019 Jiaming Liu, Yu Sun, Ulugbek S. Kamilov

We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.

Denoising Image Reconstruction

Online Regularization by Denoising with Applications to Phase Retrieval

no code implementations4 Sep 2019 Zihui Wu, Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.

Denoising Retrieval

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

no code implementations25 Aug 2019 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing.

CARL: Aggregated Search with Context-Aware Module Embedding Learning

no code implementations3 Aug 2019 Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang, Hai-Tao Zheng

To model and utilize the context information for aggregated search, we propose a model with context attention and representation learning (CARL).

Representation Learning

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

3 code implementations29 Jul 2019 Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, Haifeng Wang

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.

Chinese Named Entity Recognition Chinese Reading Comprehension +8

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

no code implementations24 Jul 2019 Junwei Pan, Yizhi Mao, Alfonso Lobos Ruiz, Yu Sun, Aaron Flores

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry.

Multi-Task Learning

RLTM: An Efficient Neural IR Framework for Long Documents

no code implementations22 Jun 2019 Chen Zheng, Yu Sun, Shengxian Wan, dianhai yu

This paper proposes a novel End-to-End neural ranking framework called Reinforced Long Text Matching (RLTM) which matches a query with long documents efficiently and effectively.

Information Retrieval Retrieval +1

Accurate Robotic Pouring for Serving Drinks

no code implementations21 Jun 2019 Yongqiang Huang, Yu Sun

Pouring is the second most frequently executed motion in cooking scenarios.

OleNet at SemEval-2019 Task 9: BERT based Multi-Perspective Models for Suggestion Mining

no code implementations SEMEVAL 2019 Jiaxiang Liu, Shuohuan Wang, Yu Sun

This paper describes our system partici- pated in Task 9 of SemEval-2019: the task is focused on suggestion mining and it aims to classify given sentences into sug- gestion and non-suggestion classes in do- main specific and cross domain training setting respectively.

Suggestion mining

Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random

no code implementations1 Jun 2019 Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi

In recommender systems, usually the ratings of a user to most items are missing and a critical problem is that the missing ratings are often missing not at random (MNAR) in reality.

Imputation Recommendation Systems

Block Coordinate Regularization by Denoising

1 code implementation NeurIPS 2019 Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.


Joint Object and State Recognition using Language Knowledge

no code implementations13 May 2019 Ahmad Babaeian Jelodar, Yu Sun

The pipeline presented in this paper includes a CNN with a double classification layer and the Concept-Net language knowledge graph on top.

Classification General Classification

Task Planning with a Weighted Functional Object-Oriented Network

1 code implementation1 May 2019 David Paulius, Kelvin Sheng Pei Dong, Yu Sun

The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort.

Robot Task Planning

KDGAN: Knowledge Distillation with Generative Adversarial Networks

no code implementations NeurIPS 2018 Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi

An alternative method is to adversarially train the classifier against a discriminator in a two-player game akin to generative adversarial networks (GAN), which can ensure the classifier to learn the true data distribution at the equilibrium of this game.

Knowledge Distillation Multi-Label Learning

Plug-In Stochastic Gradient Method

no code implementations8 Nov 2018 Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm

no code implementations31 Oct 2018 Yu Sun, Shiqi Xu, Yunzhe Li, Lei Tian, Brendt Wohlberg, Ulugbek S. Kamilov

The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.

Image Reconstruction

Image Restoration using Total Variation Regularized Deep Image Prior

no code implementations30 Oct 2018 Jiaming Liu, Yu Sun, Xiaojian Xu, Ulugbek S. Kamilov

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.

Deblurring Image Denoising +2

An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

1 code implementation12 Sep 2018 Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov

The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.

Image Reconstruction

SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage

2 code implementations22 Aug 2018 Yu Sun, Siv G. E. Andersson

Background Synonymous codon choice is mainly affected by mutation and selection.


A Survey of Knowledge Representation in Service Robotics

no code implementations5 Jul 2018 David Paulius, Yu Sun

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots.

Activity Recognition BIG-bench Machine Learning +6

Functional Object-Oriented Network: Construction & Expansion

1 code implementation5 Jul 2018 David Paulius, Ahmad Babaeian Jelodar, Yu Sun

To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge.


Neonatal Pain Expression Recognition Using Transfer Learning

no code implementations4 Jul 2018 Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun

In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning.

Face Recognition General Classification +2

Long Activity Video Understanding using Functional Object-Oriented Network

no code implementations3 Jul 2018 Ahmad Babaeian Jelodar, David Paulius, Yu Sun

Each action is therefore associated with a functional unit and the sequence of actions is further evaluated to identify the single on-going activity in the video.

Video Understanding

Stability of Scattering Decoder For Nonlinear Diffractive Imaging

no code implementations20 Jun 2018 Yu Sun, Ulugbek S. Kamilov

The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization.

Image Reconstruction

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

4 code implementations9 Jun 2018 Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu

The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.

Click-Through Rate Prediction

Construction of all-in-focus images assisted by depth sensing

no code implementations5 Jun 2018 Hang Liu, Hengyu Li, Jun Luo, Shaorong Xie, Yu Sun

A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image.

Identifying Object States in Cooking-Related Images

no code implementations17 May 2018 Ahmad Babaeian Jelodar, Md Sirajus Salekin, Yu Sun

The trained state identification model is evaluated on a subset of the Imagenet dataset and state labels are provided using a combination of the model with manual checking.

Object Recognition

Efficient and accurate inversion of multiple scattering with deep learning

4 code implementations18 Mar 2018 Yu Sun, Zhihao Xia, Ulugbek S. Kamilov

Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.

Image Reconstruction

On Calibration of Modern Neural Networks

19 code implementations ICML 2017 Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.

Document Classification General Classification

Learning to Pour

no code implementations25 May 2017 Yongqiang Huang, Yu Sun

We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring.

Multivariate Regression with Gross Errors on Manifold-valued Data

no code implementations26 Mar 2017 Xiaowei Zhang, Xudong Shi, Yu Sun, Li Cheng

Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data.


Concept Drift Adaptation by Exploiting Historical Knowledge

no code implementations12 Feb 2017 Yu Sun, Ke Tang, Zexuan Zhu, Xin Yao

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data.

Ensemble Learning Incremental Learning +1

Supervised Word Mover's Distance

1 code implementation NeurIPS 2016 Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger

Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.

Document Classification General Classification +2

Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets

no code implementations8 Sep 2016 Matteo Bianchi, Jeannette Bohg, Yu Sun

This paper reports the activities and outcomes in the Workshop on Grasping and Manipulation Datasets that was organized under the International Conference on Robotics and Automation (ICRA) 2016.

Robotic Grasping

Machine-based Multimodal Pain Assessment Tool for Infants: A Review

no code implementations1 Jul 2016 Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun, Terri Ashmeade

In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

2 code implementations TACL 2018 Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, Kilian Weinberger

To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.

Classification Cross-Lingual Document Classification +5

Deep Networks with Stochastic Depth

17 code implementations30 Mar 2016 Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger

With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4. 91% on CIFAR-10).

Image Classification

Private Causal Inference

no code implementations17 Dec 2015 Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger

Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few.

Causal Inference

Information Transfer in Swarms with Leaders

no code implementations30 Jun 2014 Yu Sun, Louis F. Rossi, Chien-Chung Shen, Jennifer Miller, X. Rosalind Wang, Joseph T. Lizier, Mikhail Prokopenko, Upul Senanayake

Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions [Couzin et al. 2005; Sun et al. 2013].

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