b'Ke Sun'

research

∙ 05/22/2023

Gloss-Free End-to-End Sign Language Translation

In this paper, we tackle the problem of sign language translation (SLT) ...

0 Kezhou Lin, et al. ∙

research

∙ 04/06/2023

InterFormer: Real-time Interactive Image Segmentation

Interactive image segmentation enables annotators to efficiently perform...

0 You Huang, et al. ∙

research

∙ 03/28/2023

FineIBT: Fine-grain Control-flow Enforcement with Indirect Branch Tracking

We present the design, implementation, and evaluation of FineIBT: a CFI ...

0 Alexander J. Gaidis, et al. ∙

research

∙ 03/24/2023

Mathematical Challenges in Deep Learning

Deep models are dominating the artificial intelligence (AI) industry sin...

0 Vahid Partovi Nia, et al. ∙

research

∙ 02/20/2023

Transformed Distribution Matching for Missing Value Imputation

We study the problem of imputing missing values in a dataset, which has ...

0 He Zhao, et al. ∙

research

∙ 11/16/2022

Some Properties of the Nash Equilibrium in 2 × 2 Zero-Sum Games

In this report, some properties of the set of Nash equilibria (NEs) of 2...

0 Ke Sun, et al. ∙

research

∙ 11/03/2022

Zero-Sum Games with Noisy Observations

In this paper, 2 × 2 zero-sum games (ZSGs) are studied under the followi...

0 Ke Sun, et al. ∙

research

∙ 10/17/2022

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy

Gaussian differential privacy (GDP) is a single-parameter family of priv...

0 YI LIU, et al. ∙

research

∙ 09/29/2022

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?

We consider the problem of learning a set of probability distributions f...

0 Ke Sun, et al. ∙

research

∙ 04/04/2022

An adaptive model checking test for functional linear model

Numerous studies have been devoted to the estimation and inference probl...

0 Enze Shi, et al. ∙

research

∙ 03/29/2022

Idea and Theory of Particle Access

Aiming at some problems existing in the current quality of service (QoS)...

0 Bo Li, et al. ∙

research

∙ 03/22/2022

Non-linear Embeddings in Hilbert Simplex Geometry

A key technique of machine learning and computer vision is to embed disc...

0 Frank Nielsen, et al. ∙

research

∙ 03/08/2022

You Cannot Always Win the Race: Analyzing the LFENCE/JMP Mitigation for Branch Target Injection

LFENCE/JMP is an existing software mitigation option for Branch Target I...

0 Alyssa Milburn, et al. ∙

research

∙ 02/21/2022

RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

Modeling stochastic traffic dynamics is critical to developing self-driv...

1 Ke Sun, et al. ∙

research

∙ 02/01/2022

Distributional Reinforcement Learning via Sinkhorn Iterations

Distributional reinforcement learning (RL) is a class of state-of-the-ar...

1 Ke Sun, et al. ∙

research

∙ 01/31/2022

Fair Wrapping for Black-box Predictions

We introduce a new family of techniques to post-process ("wrap") a black...

2 Alexander Soen, et al. ∙

research

∙ 12/27/2021

Dual Contrastive Learning for General Face Forgery Detection

With various facial manipulation techniques arising, face forgery detect...

0 Ke Sun, et al. ∙

research

∙ 12/22/2021

Asymptotic Learning Requirements for Stealth Attacks

Information-theoretic stealth attacks are data injection attacks that mi...

0 Ke Sun, et al. ∙

research

∙ 11/03/2021

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness

Adversarial robustness, which mainly contains sensitivity-based robustne...

0 Ke Sun, et al. ∙

research

∙ 10/17/2021

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

Anderson mixing has been heuristically applied to reinforcement learning...

10 Ke Sun, et al. ∙

research

∙ 10/07/2021

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm

Distributional reinforcement learning (RL) is a class of state-of-the-ar...

2 Ke Sun, et al. ∙

research

∙ 09/21/2021

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning

Abnormal states in deep reinforcement learning (RL) are states that are ...

0 Hongming Zhang, et al. ∙

research

∙ 09/17/2021

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations

In real scenarios, state observations that an agent observes may contain...

7 Ke Sun, et al. ∙

research

∙ 07/09/2021

On the Variance of the Fisher Information for Deep Learning

The Fisher information matrix (FIM) has been applied to the realm of dee...

6 Alexander Soen, et al. ∙

research

∙ 07/01/2021

Secure Quantized Training for Deep Learning

We have implemented training of neural networks in secure multi-party co...

0 Marcel Keller, et al. ∙

research

∙ 06/28/2021

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in...

0 Tieyun Qian, et al. ∙

research

∙ 04/06/2021

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

In this paper, we are interested in the bottom-up paradigm of estimating...

0 Zigang Geng, et al. ∙

research

∙ 03/07/2021

Graph Force Learning

Features representation leverages the great power in network analysis ta...

0 Ke Sun, et al. ∙

research

∙ 02/24/2021

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

The capability of image semantic segmentation may be deteriorated due to...

0 Shunxin Xu, et al. ∙

research

∙ 02/10/2021

Belief Space Planning for Mobile Robots with Range Sensors using iLQG

In this work, we use iterative Linear Quadratic Gaussian (iLQG) to plan ...

0 Ke Sun, et al. ∙

research

∙ 11/24/2020

Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements

In this paper, we address the problem of stochastic motion planning unde...

0 Ke Sun, et al. ∙

research

∙ 11/23/2020

Effectiveness of MPC-friendly Softmax Replacement

Softmax is widely used in deep learning to map some representation to a ...

0 Marcel Keller, et al. ∙

research

∙ 09/02/2020

A Practical Chinese Dependency Parser Based on A Large-scale Dataset

Dependency parsing is a longstanding natural language processing task, w...

10 Shuai Zhang, et al. ∙

research

∙ 08/09/2020

Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as...

0 Jin Xu, et al. ∙

research

∙ 07/11/2020

Feedback Enhanced Motion Planning for Autonomous Vehicles

In this work, we address the motion planning problem for autonomous vehi...

0 Ke Sun, et al. ∙

research

∙ 06/28/2020

Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates

The typical bottom-up human pose estimation framework includes two stage...

3 Ke Sun, et al. ∙

research

∙ 06/14/2020

Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data

The scarcity of class-labeled data is a ubiquitous bottleneck in a wide ...

11 Bing Yu, et al. ∙

research

∙ 12/16/2019

Seq2seq Translation Model for Sequential Recommendation

The context information such as product category plays a critical role i...

0 Ke Sun, et al. ∙

research

∙ 11/27/2019

Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions

This letter introduces an abstract learning problem called the “set embe...

16 Ke Sun, et al. ∙

research

∙ 11/21/2019

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy

Regularization plays a crucial role in machine learning models, especial...

0 Ke Sun, et al. ∙

research

∙ 10/24/2019

A Note on Our Submission to Track 4 of iDASH 2019

iDASH is a competition soliciting implementations of cryptographic schem...

0 Marcel Keller, et al. ∙

research

∙ 08/20/2019

Deep High-Resolution Representation Learning for Visual Recognition

High-resolution representations are essential for position-sensitive vis...

0 Jingdong Wang, et al. ∙

research

∙ 08/14/2019

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

The design of deep graph models still remains to be investigated and the...

0 Ke Sun, et al. ∙

research

∙ 06/04/2019

Learning Deep Image Priors for Blind Image Denoising

Image denoising is the process of removing noise from noisy images, whic...

0 Xianxu Hou, et al. ∙

research

∙ 06/04/2019

Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

We present a new method for improving the performances of variational au...

0 Xianxu Hou, et al. ∙

research

∙ 05/27/2019

Lightlike Neuromanifolds, Occam's Razor and Deep Learning

Why do deep neural networks generalize with a very high dimensional para...

3 Ke Sun, et al. ∙

research

∙ 04/30/2019

Experimental Quantum-enhanced Cryptographic Remote Control

The Internet of Things (IoT), as a cutting-edge integrated cross-technol...

0 Xiao-Ling Pang, et al. ∙

research

∙ 04/09/2019

High-Resolution Representations for Labeling Pixels and Regions

High-resolution representation learning plays an essential role in many ...

6 Ke Sun, et al. ∙

research

∙ 03/11/2019

Fisher-Bures Adversary Graph Convolutional Networks

In a graph convolutional network, we assume that the graph G is generate...

0 Ke Sun, et al. ∙

research

∙ 02/28/2019

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification

The effectiveness of Graph Convolutional Networks (GCNs) has been demons...

0 Ke Sun, et al. ∙

Ke Sun

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