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ORCIDSang (Peter) Chin
Sang Peter Chin – Sang Chin 0001 – Peter Chin 0001
Person information
- affiliation: Dartmouth College, School of Engineering, Hanover, NH, USA
- affiliation (former): Charles Stark Draper Laboratory, Cambridge, MA, USA
- affiliation (former): Boston University, Department of Computer Science, MA, USA
- affiliation (former): Johns Hopkins University, Applied Physics Laboratory, Baltimore, MD, USA
- affiliation (PhD 1998): Massachusetts Institute of Technology, Cambridge, MA, USA
Other persons with the same name
- Peter Chin 0002 — Novartis Pharmaceuticals, East Hanover, NJ, USA
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2020 – today
- 2024
[j9]Xiao Zhou, Akshara Rao Balachandra
, Michael F. Romano, Sang Peter Chin, Rhoda Au, Vijaya B. Kolachalama:
Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals. IEEE Access 12: 83169-83182 (2024)- [c75]Quang Truong, Peter Chin:
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes. AAAI 2024: 15382-15391 - [c74]Judith Herrmann, Raphael Kunert, Ron Hachmon, Aviv Markus, Allison Gunby-Mann, Sarel Cohen, Tobias Friedrich, Peter Chin:
Detecting Continuous Gravitational Waves Using Generated Training Data. ICASSP 2024: 146-150 - [c73]Junyan Cheng, Peter Chin:
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series. ICLR 2024 - [c72]Junyan Cheng, Peter Chin:
Bridging Neural and Symbolic Representations with Transitional Dictionary Learning. ICLR 2024 - [i42]Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane:
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. CoRR abs/2402.02441 (2024) - [i41]Moshik Hershcovitch, Leshem Choshen, Andrew Wood, Ilias Enmouri, Peter Chin, Swaminathan Sundararaman, Danny Harnik:
Lossless and Near-Lossless Compression for Foundation Models. CoRR abs/2404.15198 (2024) - [i40]Ryan Yu, Alex Olshevsky, Peter Chin:
Tree Search for Simultaneous Move Games via Equilibrium Approximation. CoRR abs/2406.10411 (2024) - [i39]Junyan Cheng, Peter Chin:
AAPM: Large Language Model Agent-based Asset Pricing Models. CoRR abs/2409.17266 (2024) - 2023
- [c71]Ikeoluwa Abioye, Allison Gunby-Mann, Xu Wang, Sarel Cohen, Peter Chin:
Learned Approximate Distance Labels for Graphs. COMPLEX NETWORKS (1) 2023: 339-350 - [c70]Minh N. Bui, Dung N. Tran, Kazuhito Koishida, Trac D. Tran, Peter Chin:
Improving Low-Latency Mono-Channel Speech Enhancement by Compensation Windows in STFT Analysis. COMPLEX NETWORKS (1) 2023: 363-373 - [c69]Davin Jeong, Allison Gunby-Mann, Sarel Cohen, Maximilian Katzmann, Chau Pham, Arnav Bhakta, Tobias Friedrich, Peter Chin:
Deep Distance Sensitivity Oracles. COMPLEX NETWORKS (1) 2023: 452-463 - [c68]Frank Pacini, Allison Gunby-Mann, Sarel Cohen, Peter Chin:
ANEDA: Adaptable Node Embeddings for Shortest Path Distance Approximation. HPEC 2023: 1-7 - [i38]Pedro Colon-Hernandez, Henry Lieberman, Yida Xin, Claire Yin, Cynthia Breazeal, Peter Chin:
Adversarial Transformer Language Models for Contextual Commonsense Inference. CoRR abs/2302.05406 (2023) - [i37]Junyan Cheng, Peter Chin:
On the Transition from Neural Representation to Symbolic Knowledge. CoRR abs/2308.02000 (2023) - [i36]Quang Truong, Peter Chin:
Generalizing Topological Graph Neural Networks with Paths. CoRR abs/2308.06838 (2023) - 2022
- [c67]Laura Greige, Fernando De Mesentier Silva, Meredith Trotter, Chris Lawrence, Peter Chin, Dilip Varadarajan:
Collusion Detection in Team-Based Multiplayer Games. AAAI Spring Symposium: MAKE 2022 - [c66]Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin, Maryam Khademi:
A Study on Self-Supervised Object Detection Pretraining. ECCV Workshops (4) 2022: 86-99 - [c65]Hieu Le, Dieu-Thu Le, Verena Weber, Chris Church, Kay Rottmann, Melanie Bradford, Peter Chin:
Semi-supervised Adversarial Text Generation based on Seq2Seq models. EMNLP (Industry Track) 2022: 254-262 - [c64]Andrew Wood, Moshik Hershcovitch, Ilias Ennmouri, Weiyu Zong, Saurav Chennuri, Sarel Cohen, Swaminathan Sundararaman, Daniel G. Waddington, Sang (Peter) Chin:
Towards Fast Crash-Consistent Cluster Checkpointing. HPEC 2022: 1-8 - [c63]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. ICASSP 2022: 4338-4342 - [c62]Trung Dang, Dung N. Tran, Peter Chin, Kazuhito Koishida:
Training Robust Zero-Shot Voice Conversion Models with Self-Supervised Features. ICASSP 2022: 6557-6561 - [c61]Ryan Yu, Andrew Wood, Sarel Cohen, Moshik Hershcovitch, Daniel G. Waddington, Peter Chin:
Biologically Plausible Complex-Valued Neural Networks and Model Optimization. AIAI (1) 2022: 369-382 - [c60]Xiao Wang, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. PCS 2022: 97-101 - [i35]Hieu Le, Hans Walker, Dung N. Tran, Peter Chin:
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness. CoRR abs/2201.01399 (2022) - [i34]Andrew Wood, Moshik Hershcovitch, Daniel G. Waddington, Sarel Cohen, Peter Chin:
Non-Volatile Memory Accelerated Posterior Estimation. CoRR abs/2202.10522 (2022) - [i33]Andrew Wood, Moshik Hershcovitch, Daniel G. Waddington, Sarel Cohen, Meredith Wolf, Hongjun Suh, Weiyu Zong, Peter Chin:
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis. CoRR abs/2202.11518 (2022) - [i32]Laura Greige, Fernando De Mesentier Silva, Meredith Trotter, Chris Lawrence, Peter Chin, Dilip Varadarajan:
Collusion Detection in Team-Based Multiplayer Games. CoRR abs/2203.05121 (2022) - [i31]Hieu Le, Taufiq Husada Daryanto, Fabian Zhafransyah, Derry Wijaya, Elizabeth Coppock, Sang Chin:
Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach. CoRR abs/2205.07795 (2022) - [i30]Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin, Maryam Khademi:
A Study on Self-Supervised Object Detection Pretraining. CoRR abs/2207.04186 (2022) - [i29]Chau Pham, Trung Dang, Peter Chin:
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks. CoRR abs/2209.14264 (2022) - [i28]Davin Jeong, Chau Pham, Arnav Bhakta, Sarel Cohen, Maximilian Katzmann, Tobias Friedrich, Sang (Peter) Chin:
Deep Distance Sensitivity Oracles. CoRR abs/2211.02681 (2022) - [i27]Weiyu Zong, Mingqian Feng, Griffin Heyrich, Peter Chin:
cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations. CoRR abs/2212.02567 (2022) - 2021
- [j8]Shan Huang, Xiao Zhou, Sang Chin:
Application of Seq2Seq Models on Code Correction. Frontiers Artif. Intell. 4: 590215 (2021) - [c59]Pedro Colon-Hernandez, Yida Xin, Henry Lieberman, Catherine Havasi, Cynthia Breazeal, Peter Chin:
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations. ACL/IJCNLP (Findings) 2021: 2086-2095 - [c58]Siyue Wang, Pu Zhao, Xiao Wang, Sang Chin, Thomas Wahl, Yunsi Fei, Qi Alfred Chen, Xue Lin:
Intrinsic Examples: Robust Fingerprinting of Deep Neural Networks. BMVC 2021: 46 - [c57]Michael R. Douglas, Michael Simkin, Omri Ben-Eliezer, Tianqi Wu, Peter Chin, Trung V. Dang, Andrew Wood:
What Is Learned in Knowledge Graph Embeddings? COMPLEX NETWORKS 2021: 587-602 - [c56]Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin:
Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning. COMPLEX NETWORKS 2021: 657-668 - [c55]Sarel Cohen, Moshik Hershcovitch, Martin Taraz, Otto Kißig, Andrew Wood, Daniel G. Waddington, Peter Chin, Tobias Friedrich:
Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory. COMPLEX NETWORKS 2021: 742-753 - [c54]Laura Greige, Peter Chin:
Deep Reinforcement Learning for FlipIt Security Game. COMPLEX NETWORKS 2021: 831-843 - [c53]Hieu Le, Andrew Wood, Sylee Dandekar, Peter Chin:
Neural Network Optimization with Biologically Inspired Low-Dimensional Manifold Learning. CSCI 2021: 8-13 - [c52]Hieu Le, Hans Walker, Dung N. Tran, Peter Chin:
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness. CSCI 2021: 1594-1599 - [c51]Andrew Wood, Moshik Hershcovitch, Daniel G. Waddington, Sarel Cohen, Meredith Wolf, Hongjun Suh, Weiyu Zong, Peter Chin:
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis. HPEC 2021: 1-7 - [c50]Peilun Dai, Sang Chin:
Training Many-to-Many Recurrent Neural Networks with Target Propagation. ICANN (4) 2021: 433-443 - [c49]Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung T. Phan, Sang (Peter) Chin, Trac D. Tran:
A Scale Invariant Measure of Flatness for Deep Network Minima. ICASSP 2021: 1680-1684 - [c48]Louis Jensen, Jayme Fosa, Ben Teitelbaum, Peter Chin:
How Dense Autoencoders can still Achieve the State-of-the-art in Time-Series Anomaly Detection. ICMLA 2021: 1272-1277 - [c47]Xiao Zhou, Xiao Wang, Gavin Brown, Chengchen Wang, Peter Chin:
Mixed Spatio-Temporal Neural Networks on Real-time Prediction of Crimes. ICMLA 2021: 1749-1754 - [c46]Yida Xin, Henry Lieberman, Peter Chin:
PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers. KR 2021: 712-716 - [c45]Peter Chin, Emily Do, Cody Doucette, Brandon Kalashian, David Last, Nathan Lenz, Edward Lu, Devon Minor, Elias Noyes, Colleen T. Rock, Nathaniel Soule, Nicholas Walczak, Dave Canestrare:
TAK-ML: Applying Machine Learning at the Tactical Edge. MILCOM 2021: 108-114 - [c44]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. NeurIPS 2021: 1727-1738 - [i26]Yida Xin, Henry Lieberman, Peter Chin:
Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge. CoRR abs/2102.00924 (2021) - [i25]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It. CoRR abs/2104.07815 (2021) - [i24]Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. CoRR abs/2105.07512 (2021) - [i23]Pedro Colon-Hernandez, Yida Xin, Henry Lieberman, Catherine Havasi, Cynthia Breazeal, Peter Chin:
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations. CoRR abs/2108.12941 (2021) - [i22]Michael R. Douglas, Michael Simkin, Omri Ben-Eliezer, Tianqi Wu, Peter Chin, Trung V. Dang, Andrew Wood:
What is Learned in Knowledge Graph Embeddings? CoRR abs/2110.09978 (2021) - [i21]Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin:
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning. CoRR abs/2110.11536 (2021) - [i20]Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays:
Revealing and Protecting Labels in Distributed Training. CoRR abs/2111.00556 (2021) - [i19]Trung Dang, Dung N. Tran, Peter Chin, Kazuhito Koishida:
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features. CoRR abs/2112.04424 (2021) - 2020
- [c43]Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin:
AdvMS: A Multi-Source Multi-Cost Defense Against Adversarial Attacks. ICASSP 2020: 2902-2906 - [c42]Louis Jensen, Jacob Harer, Sang Chin:
NodeDrop: A Method for Finding Sufficient Network Architecture Size. IJCNN 2020: 1-9 - [i18]Shan Huang, Xiao Zhou, Sang Chin:
A Study of Pyramid Structure for Code Correction. CoRR abs/2001.11367 (2020) - [i17]Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin:
Block Switching: A Stochastic Approach for Deep Learning Security. CoRR abs/2002.07920 (2020) - [i16]Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin:
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks. CoRR abs/2002.08439 (2020) - [i15]Laura Greige, Peter Chin:
Reinforcement Learning in FlipIt. CoRR abs/2002.12909 (2020) - [i14]Andrew Wood, Ali Sydney, Peter Chin, Bishal Thapa, Ryan Ross:
GymFG: A Framework with a Gym Interface for FlightGear. CoRR abs/2004.12481 (2020)
2010 – 2019
- 2019
- [j7]Xavier F. Cadet, Réda Dehak, Sang Peter Chin, Miloud Bessafi:
Non-Linear Dynamics Analysis of Protein Sequences. Application to CYP450. Entropy 21(9): 852 (2019) - [c41]Luoluo Liu, Sang Peter Chin, Trac D. Tran:
Reducing Sampling Ratios and Increasing Number of Estimates Improve Bagging in Sparse Regression. CISS 2019: 1-5 - [c40]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. IJCAI 2019: 6013-6019 - [c39]Luoluo Liu, Sang Peter Chin, Trac D. Tran:
JOBS: Joint-Sparse Optimization from Bootstrap Samples. ISIT 2019: 2689-2693 - [i13]Louis Jensen, Jacob Harer, Sang Chin:
NodeDrop: A Condition for Reducing Network Size without Effect on Output. CoRR abs/1906.01026 (2019) - [i12]Jacob Harer, Christopher P. Reale, Peter Chin:
Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data. CoRR abs/1908.00449 (2019) - [i11]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Peter Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. CoRR abs/1908.07116 (2019) - 2018
- [c38]Weicheng Ma, Kai Cao, Xiang Li, Peter Chin:
Tree Structured Multimedia Signal Modeling. FLAIRS 2018: 184-188 - [c37]Arun Asokan Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, Muyinatu A. Lediju Bell, Trac D. Tran:
Reconstruction-Free Deep Convolutional Neural Networks for Partially Observed Images. GlobalSIP 2018: 400-404 - [c36]Sang (Peter) Chin, Trac D. Tran, Dung N. Tran, Akshay Rangamani:
A Greedy Pursuit Algorithm for Separating Signals from Nonlinear Compressive Observations. ICASSP 2018: 2171-2175 - [c35]Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David R. Kaeli, Peter Chin, Xue Lin:
Defensive dropout for hardening deep neural networks under adversarial attacks. ICCAD 2018: 71:1-71:8 - [c34]Xiao Wang, Jie Zhang, Tao Xiong, Trac Duy Tran, Sang Peter Chin, Ralph Etienne-Cummings:
Using Deep Learning to Extract Scenery Information in Real Time Spatiotemporal Compressed Sensing. ISCAS 2018: 1-4 - [c33]Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Ashish Arora, Tejaswini Ganapathi, Sang (Peter) Chin, Trac D. Tran:
Sparse Coding and Autoencoders. ISIT 2018: 36-40 - [c32]Weicheng Ma, Kai Cao, Zhaoheng Ni, Peter Chin, Xiang Li:
Sound Signal Processing with Seq2Tree Network. LREC 2018 - [c31]Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin:
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. NeurIPS 2018: 7944-7954 - [i10]Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell, Onur Ozdemir, Leonard R. Kosta, Akshay Rangamani, Lei H. Hamilton, Gabriel I. Centeno, Jonathan R. Key, Paul M. Ellingwood, Marc W. McConley, Jeffrey M. Opper, Peter Chin, Tomo Lazovich:
Automated software vulnerability detection with machine learning. CoRR abs/1803.04497 (2018) - [i9]Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin:
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. CoRR abs/1805.07475 (2018) - [i8]Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David R. Kaeli, Peter Chin, Xue Lin:
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks. CoRR abs/1809.05165 (2018) - [i7]Luoluo Liu, Sang (Peter) Chin, Trac D. Tran:
JOBS: Joint-Sparse Optimization from Bootstrap Samples. CoRR abs/1810.03743 (2018) - [i6]Luoluo Liu, Sang (Peter) Chin, Trac D. Tran:
Reducing Sampling Ratios and Increasing Number of Estimates Improve Bagging in Sparse Regression. CoRR abs/1812.08808 (2018) - 2017
- [j6]Sang Peter Chin, Jonathan Cohen, Alison Albin, Mykola Hayvanovych, Elizabeth Reilly, Gavin Brown, Jacob Harer:
A Mathematical Analysis of Network Controllability Through Driver Nodes. IEEE Trans. Comput. Soc. Syst. 4(2): 40-51 (2017) - [c30]Xiao Zhou, Chengchen Wang, Yiteng Xu, Xiao Wang, Peter Chin:
Domain specific inpainting with concurrently pretrained generative adversarial networks. GlobalSIP 2017: 1185-1189 - [c29]Dung N. Tran, Sang Peter Chin, Trac D. Tran:
A provable nonconvex model for factoring nonnegative matrices. ICASSP 2017: 2262-2266 - [c28]Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, Shaohua Kevin Zhou, Zhoubing Xu, Jin Hyeong Park, Mingqing Chen, Trac D. Tran, Sang Peter Chin, Dimitris N. Metaxas, Dorin Comaniciu:
Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization. IPMI 2017: 633-644 - [c27]Tao Xiong, Jie Zhang, Chetan Singh Thakur, John M. Rattray, Sang Peter Chin, Trac D. Tran, Ralph Etienne-Cummings:
Live demonstration: A compact all-CMOS spatiotemporal compressed sensing video camera. ISCAS 2017: 1 - [c26]Dong Yang, Tao Xiong, Daguang Xu, Shaohua Kevin Zhou, Zhoubing Xu, Mingqing Chen, Jin Hyeong Park, Sasa Grbic, Trac D. Tran, Sang Peter Chin, Dimitris N. Metaxas, Dorin Comaniciu:
Deep Image-to-Image Recurrent Network with Shape Basis Learning for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes. MICCAI (3) 2017: 498-506 - [c25]Tao Xiong, John M. Rattray, Jie Zhang, Chetan Singh Thakur, Sang Peter Chin, Trac D. Tran, Ralph Etienne-Cummings:
Spatiotemporal compressed sensing for video compression. MWSCAS 2017: 289-292 - [i5]Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, Shaohua Kevin Zhou, Zhoubing Xu, Jin Hyeong Park, Mingqing Chen, Trac D. Tran, Sang Peter Chin, Dimitris N. Metaxas, Dorin Comaniciu:
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization. CoRR abs/1705.05998 (2017) - [i4]Akshay Rangamani, Anirbit Mukherjee, Ashish Arora, Tejaswini Ganapathy, Amitabh Basu, Sang (Peter) Chin, Trac D. Tran:
Critical Points Of An Autoencoder Can Provably Recover Sparsely Used Overcomplete Dictionaries. CoRR abs/1708.03735 (2017) - 2016
- [j5]Paul Bendich, Sang Peter Chin, Jesse Clarke, Jonathan deSena, John Harer, Elizabeth Munch, Andrew Newman, David Porter, David Rouse, Nate Strawn, Adam Watkins:
Topological and statistical behavior classifiers for tracking applications. IEEE Trans. Aerosp. Electron. Syst. 52(6): 2644-2661 (2016) - [j4]Xilin Liu, Milin Zhang, Tao Xiong, Andrew G. Richardson, Timothy H. Lucas, Sang Peter Chin, Ralph Etienne-Cummings, Trac D. Tran, Jan Van der Spiegel:
A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface. IEEE Trans. Biomed. Circuits Syst. 10(4): 874-883 (2016) - [c24]Louis Y. Kim, Jacob Harer, Akshay Rangamani, James Moran, Philip D. Parks, Alik Widge, Emad N. Eskandar, Darin D. Dougherty, Sang (Peter) Chin:
Predicting local field potentials with recurrent neural networks. EMBC 2016: 808-811 - [c23]Dung N. Tran, Luoluo Liu, Trac D. Tran, Sang (Peter) Chin, Jeffrey Korn, Eric T. Hoke:
Compressive coding via random replicate mirror. GlobalSIP 2016: 148-152 - [c22]Luoluo Liu, Trac D. Tran, Sang (Peter) Chin:
Partial face recognition: A sparse representation-based approach. ICASSP 2016: 2389-2393 - [c21]Dung N. Tran, Shuai Huang, Sang Peter Chin, Trac D. Tran:
Low-rank matrices recovery via entropy function. ICASSP 2016: 4064-4068 - [c20]Daniel R. Mendat, Sang Chin, Steve B. Furber, Andreas G. Andreou:
Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors. LASCAS 2016: 399-402 - [c19]Guevara Noubir, Muriel Médard, Peter Chin:
Wi(deband)-Fi: A Proposal for an Opportunistic Wideband Architecture based on Wi-Fi. MobiWac 2016: 115-122 - 2015
- [c18]Minh D. Dao, Akshay Rangamani, Sang Peter Chin, Nam P. Nguyen, Trac D. Tran:
Targeted Dot Product Representation for Friend Recommendation in Online Social Networks. ASONAM 2015: 349-356 - [c17]Tao Xiong, Jie Zhang, Sang Chin, Trac D. Tran, Ralph Etienne-Cummings:
Compressed sensing block-wise exposure control algorithm using optical flow estimation. BioCAS 2015: 1-4 - [c16]Bryan T. Bosworth, Jasper R. Stroud, Dung N. Tran, Trac D. Tran, Sang Chin, Mark A. Foster:
High-speed compressed sensing measurement using spectrally-encoded ultrafast laser pulses. CISS 2015: 1-4 - [c15]Daniel R. Mendat, Sang Chin, Steve B. Furber, Andreas G. Andreou:
Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture. CISS 2015: 1-6 - [c14]Peter Chin, Anup Rao, Van Vu:
Stochastic Block Model and Community Detection in Sparse Graphs: A spectral algorithm with optimal rate of recovery. COLT 2015: 391-423 - [c13]Lei H. Hamilton, Marc W. McConley, Kai Angermueller, David Goldberg, Massimiliano Corba, Louis Y. Kim, James Moran, Philip D. Parks, Sang (Peter) Chin, Alik S. Widge, Darin D. Dougherty, Emad N. Eskandar:
Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system. EMBC 2015: 7831-7836 - [c12]Dung N. Tran, Tao Xiong, Sang Peter Chin, Trac D. Tran:
Nonnegative matrix factorization with gradient vertex pursuit. ICASSP 2015: 2125-2129 - [c11]Dung N. Tran, Duyet N. Tran, Sang Peter Chin, Trac D. Tran:
Local sensing with global recovery. ICIP 2015: 4313-4316 - [c10]Andrew Mastin, Patrick Jaillet, Sang Chin:
Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty. ISAAC 2015: 491-501 - [c9]Tao Xiong, Jie Zhang, Yuanming Suo, Dung N. Tran, Ralph Etienne-Cummings, Sang Chin, Trac D. Tran:
An unsupervised dictionary learning algorithm for neural recordings. ISCAS 2015: 1010-1013 - [i3]Peter Chin, Anup Rao, Van Vu:
Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery. CoRR abs/1501.05021 (2015) - 2014
- [j3]Jie Zhang, Yuanming Suo, Srinjoy Mitra, Sang (Peter) Chin, Steven Hsiao, Refet Firat Yazicioglu, Trac D. Tran, Ralph Etienne-Cummings:
An Efficient and Compact Compressed Sensing Microsystem for Implantable Neural Recordings. IEEE Trans. Biomed. Circuits Syst. 8(4): 485-496 (2014) - [j2]Yuanming Suo, Jie Zhang, Tao Xiong, Sang Peter Chin, Ralph Etienne-Cummings, Trac D. Tran:
Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings. IEEE Trans. Biomed. Circuits Syst. 8(5): 648-659 (2014) - [c8]Minh Dao, Yuanming Suo, Sang (Peter) Chin, Trac D. Tran:
Structured sparse representation with low-rank interference. ACSSC 2014: 106-110 - [c7]Tao Xiong, Yuanming Suo, Jie Zhang, Siwei Liu, Ralph Etienne-Cummings, Sang Chin, Trac D. Tran:
A dictionary learning algorithm for multi-channel neural recordings. BioCAS 2014: 9-12 - [c6]Edward Boehnlein, Peter Chin, Amit Sinha, Linyuan Lu:
Computing Diffusion State Distance Using Green's Function and Heat Kernel on Graphs. WAW 2014: 79-95 - [i2]Andrew Mastin, Patrick Jaillet, Sang Chin:
Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty. CoRR abs/1401.7043 (2014) - [i1]Paul Bendich, Sang Chin, Jesse Clarke, Jonathan deSena, John Harer, Elizabeth Munch, Andrew Newman, David Porter, David Rouse, Nate Strawn, Adam Watkins:
Topological and Statistical Behavior Classifiers for Tracking Applications. CoRR abs/1406.0214 (2014) - 2013
- [c5]Yuanming Suo, Jie Zhang, Ralph Etienne-Cummings, Trac D. Tran, Sang Chin:
Energy-efficient two-stage Compressed Sensing method for implantable neural recordings. BioCAS 2013: 150-153 - [c4]Minh Dao, Yuanming Suo, Sang Chin, Trac D. Tran:
Video frame interpolation via weighted robust principal component analysis. ICASSP 2013: 1404-1408 - [c3]Jie Zhang, Yuanming Suo, Srinjoy Mitra, Sang (Peter) Chin, Trac D. Tran, Refet Firat Yazicioglu, Ralph Etienne-Cummings:
Reconstruction of neural action potentials using signal dependent sparse representations. ISCAS 2013: 1520-1523 - 2012
- [j1]Garrick Orchard, Jie Zhang, Yuanming Suo, Minh Dao, Dzung T. Nguyen, Sang Chin, Christoph Posch, Trac D. Tran, Ralph Etienne-Cummings:
Real Time Compressive Sensing Video Reconstruction in Hardware. IEEE J. Emerg. Sel. Topics Circuits Syst. 2(3): 604-615 (2012) - [c2]Minh Dao, Dzung T. Nguyen, Trac D. Tran, Sang Chin:
Chemical plume detection in hyperspectral imagery via joint sparse representation. MILCOM 2012: 1-5 - [c1]Guangliang Chen, Mark A. Iwen, Sang Chin, Mauro Maggioni:
A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements. VCIP 2012: 1-6
Coauthor Index
Trac D. Tran
aka: Trac Duy Tran
aka: Trac Duy Tran
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