b'Yi Sun'

research

∙ 07/16/2022

TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance

Grasp pose estimation is an important issue for robots to interact with ...

Hongtao Wen, et al. ∙

research

∙ 06/17/2022

Accelerated Kinetic Monte Carlo methods for general nonlocal traffic flow models

This paper presents a class of one-dimensional cellular automata (CA) mo...

Yi Sun, et al. ∙

research

∙ 05/12/2022

Using Natural Sentences for Understanding Biases in Language Models

Evaluation of biases in language models is often limited to syntheticall...

Sarah Alnegheimish, et al. ∙

research

∙ 09/08/2021

NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task–Next Sentence Prediction

Using prompts to utilize language models to perform various downstream t...

Yi Sun, et al. ∙

research

∙ 07/02/2021

Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM

Motivated by applications to single-particle cryo-electron microscopy (c...

Zhou Fan, et al. ∙

research

∙ 06/18/2021

ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems

A novel intercarrier interference (ICI)-aware orthogonal frequency divis...

Yi Sun, et al. ∙

research

∙ 03/25/2021

Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation

Prior works on formalizing explanations of a graph neural network (GNN) ...

Yi Sun, et al. ∙

research

∙ 10/21/2020

Data augmentation as stochastic optimization

We present a theoretical framework recasting data augmentation as stocha...

Boris Hanin, et al. ∙

research

∙ 06/25/2020

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40 is attributed to Heating, Ventilation,...

Liang Yu, et al. ∙

research

∙ 03/31/2020

Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model

We study the non-convex optimization landscape for maximum likelihood es...

Zhou Fan, et al. ∙

research

∙ 02/13/2020

Multi-objective Ranking via Constrained Optimization

In this paper, we introduce an Augmented Lagrangian based method to inco...

Michinari Momma, et al. ∙

research

∙ 09/14/2019

Line as object: datasets and framework for semantic line segment detection

In this work, we propose a learning-based approach to the task of detect...

Yi Sun, et al. ∙

research

∙ 08/21/2019

Testing Robustness Against Unforeseen Adversaries

Considerable work on adversarial defense has studied robustness to a fix...

Daniel Kang, et al. ∙

research

∙ 08/19/2019

Learning Fair Classifiers in Online Stochastic Settings

In many real life situations, including job and loan applications, gatek...

Yi Sun, et al. ∙

research

∙ 06/15/2019

Txilm: Lossy Block Compression with Salted Short Hashing

Current blockchains are restricted by the low throughput. Aimed at this ...

Donghui Ding, et al. ∙

research

∙ 05/03/2019

Transfer of Adversarial Robustness Between Perturbation Types

We study the transfer of adversarial robustness of deep neural networks ...

Daniel Kang, et al. ∙

research

∙ 03/22/2019

Principal components in linear mixed models with general bulk

We study the outlier eigenvalues and eigenvectors in variance components...

Zhou Fan, et al. ∙

research

∙ 12/12/2018

An Active-Passive Measurement Study of TCP Performance over LTE on High-speed Rails

High-speed rail (HSR) systems potentially provide a more efficient way o...

Jing Wang, et al. ∙

research

∙ 12/04/2018

Learning Vine Copula Models For Synthetic Data Generation

A vine copula model is a flexible high-dimensional dependence model whic...

Yi Sun, et al. ∙

research

∙ 11/20/2018

Limited Gradient Descent: Learning With Noisy Labels

Label noise may handicap the generalization of classifiers, and it is an...

Yi Sun, et al. ∙

research

∙ 06/25/2018

Spiked covariances and principal components analysis in high-dimensional random effects models

We study principal components analyses in multivariate random and mixed ...

Zhou Fan, et al. ∙

research

∙ 03/09/2017

Development of a computer-aided design software for dental splint in orthognathic surgery

In the orthognathic surgery, dental splints are important and necessary ...

Xiaojun Chen, et al. ∙

research

∙ 12/07/2015

Sparsifying Neural Network Connections for Face Recognition

This paper proposes to learn high-performance deep ConvNets with sparse ...

Yi Sun, et al. ∙

research

∙ 11/02/2015

From random walks to distances on unweighted graphs

Large unweighted directed graphs are commonly used to capture relations ...

Tatsunori B. Hashimoto, et al. ∙

research

∙ 02/03/2015

DeepID3: Face Recognition with Very Deep Neural Networks

The state-of-the-art of face recognition has been significantly advanced...

Yi Sun, et al. ∙

research

∙ 12/03/2014

Deeply learned face representations are sparse, selective, and robust

This paper designs a high-performance deep convolutional network (DeepID...

Yi Sun, et al. ∙

research

∙ 11/20/2014

Metric recovery from directed unweighted graphs

We analyze directed, unweighted graphs obtained from x_i∈R^d by connecti...

Tatsunori B. Hashimoto, et al. ∙

research

∙ 06/18/2014

Deep Learning Face Representation by Joint Identification-Verification

The key challenge of face recognition is to develop effective feature re...

Yi Sun, et al. ∙

research

∙ 09/26/2012

Efficient Natural Evolution Strategies

Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...

Yi Sun, et al. ∙

research

∙ 06/18/2012

On the Size of the Online Kernel Sparsification Dictionary

We analyze the size of the dictionary constructed from online kernel spa...

Yi Sun, et al. ∙

research

∙ 06/22/2011

Natural Evolution Strategies

This paper presents Natural Evolution Strategies (NES), a recent family ...

Daan Wierstra, et al. ∙

research

∙ 06/10/2011

A Linear Time Natural Evolution Strategy for Non-Separable Functions

We present a novel Natural Evolution Strategy (NES) variant, the Rank-On...

Yi Sun, et al. ∙

research

∙ 03/29/2011

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

To maximize its success, an AGI typically needs to explore its initially...

Yi Sun, et al. ∙

Yi Sun

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