J. Sun

Publications
Influence

Transfer Feature Learning with Joint Distribution Adaptation

Mingsheng Long, J. Wang, G. Ding, J. Sun, Philip S. Yu
Mathematics, Computer Science
IEEE International Conference on Computer Vision
1 December 2013

We put forward a novel transfer learning solution, referred to as Joint Distribution Adaptation (JDA), to jointly adapt both the marginal and conditional distributions in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Expand

Transfer Joint Matching for Unsupervised Domain Adaptation

Mingsheng Long, J. Wang, G. Ding, J. Sun, Philip S. Yu
Mathematics, Computer Science
IEEE Conference on Computer Vision and Pattern…
23 June 2014

We propose a novel domain adaptation solution, referred to as Transfer Joint Matching (TJM), to jointly perform feature matching and instance reweighting across domains in a principled dimensionality reduction procedure. Expand

Transfer Sparse Coding for Robust Image Representation

Mingsheng Long, G. Ding, J. Wang, J. Sun, Yuchen Guo, Philip S. Yu
Mathematics, Computer Science
IEEE Conference on Computer Vision and Pattern…
23 June 2013

Sparse coding learns a set of basis functions such that each input signal can be well approximated by a linear combination of just a few of the bases. Expand

Domain Invariant Transfer Kernel Learning

Mingsheng Long, J. Wang, J. Sun, Philip S. Yu
Computer Science
IEEE Transactions on Knowledge and Data…
1 June 2015

We propose a transfer kernel learning (TKL) approach to learn a domain-invariant kernel by directly matching source and target distributions in the reproducing kernel Hilbert space. Expand

A novel approach for process mining based on event types

Lijie Wen, J. Wang, W. V. Aalst, B. Huang, J. Sun
Computer Science, Engineering
IEEE International Conference on Services…
1 April 2009

A novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Expand

A workflow net similarity measure based on transition adjacency relations

H. Zha, J. Wang, Lijie Wen, C. Wang, J. Sun
Computer Science
Comput. Ind.
1 June 2010

We define the similarity and the distance based on firing sequences in the context of workflow nets (WF-nets) as the unified reference concepts. Expand

Mining process models with non-free-choice constructs

Lijie Wen, W. V. Aalst, J. Wang, J. Sun
Computer Science
Data Mining and Knowledge Discovery
1 October 2007

We propose an algorithm that is able to deal with both kinds of causal dependencies between tasks, i.e., explicit and implicit ones. Expand

Deep Learning of Transferable Representation for Scalable Domain Adaptation

Mingsheng Long, J. Wang, Yue Cao, J. Sun, Philip S. Yu
Computer Science
IEEE Transactions on Knowledge and Data…
1 August 2016

We propose a unified deep adaptation framework for jointly learning transferable representation and classifier to enable scalable domain adaptation, by taking the advantages of both deep learning and optimal two-sample matching. Expand

DLFuzz: differential fuzzing testing of deep learning systems

Jianmin Guo, Y. Jiang, Yue Zhao, Quan Chen, J. Sun
Computer Science
ESEC/SIGSOFT FSE
28 August 2018

In this paper, we propose DLFuzz, the first differential fuzzing testing framework to guide DL systems exposing incorrect behaviors. Expand

Overlap-free Karatsuba-Ofman polynomial multiplication algorithms

H. Fan, J. Sun, M. Gu, K. Lam
Mathematics, Computer Science
IET Inf. Secur.
8 January 2010

The authors describe how a simple way to split input operands allows for fast VLSI implementations of subquadratic GF (2)[ x ] Karatsuba-Ofman multipliers. Expand

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