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Books and Theses
- 2011
[b1]Renqiang Min:
Machine Learning Approaches to Biological Sequence and Phenotype Data Analysis. University of Toronto, Canada, 2011
Journal Articles
- 2023
- [j11]Filippo Grazioli
, Pierre Machart, Anja Mösch, Kai Li, Leonardo V. Castorina, Nico Pfeifer, Martin Renqiang Min:
Attentive Variational Information Bottleneck for TCR-peptide interaction prediction. Bioinform. 39(1) (2023) - [j10]Ziqi Chen, Baoyi Zhang, Hongyu Guo, Prashant S. Emani, Trevor Clancy, Chongming Jiang, Mark Gerstein, Xia Ning, Chao Cheng, Martin Renqiang Min:
Binding peptide generation for MHC Class I proteins with deep reinforcement learning. Bioinform. 39(2) (2023) - 2021
- [j9]Zhanlin Chen, Jing Zhang, Jason Liu, Yi Dai, Donghoon Lee, Martin Renqiang Min, Min Xu, Mark Gerstein:
DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays. Bioinform. 37(Supplement): 280-288 (2021) - [j8]Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning:
A deep generative model for molecule optimization via one fragment modification. Nat. Mach. Intell. 3(12): 1040-1049 (2021) - 2017
- [j7]Linnan Wang, Yi Yang, Renqiang Min, Srimat T. Chakradhar:
Accelerating deep neural network training with inconsistent stochastic gradient descent. Neural Networks 93: 219-229 (2017) - 2015
- [j6]Pavel P. Kuksa, Martin Renqiang Min, Rishabh Dugar, Mark Gerstein:
High-order neural networks and kernel methods for peptide-MHC binding prediction. Bioinform. 31(22): 3600-3607 (2015) - 2012
- [j5]Ke Jin, Jingjing Li, Frederick S. Vizeacoumar, Zhijian Li, Renqiang Min, Lee Zamparo, Franco J. Vizeacoumar, Alessandro Datti, Brenda J. Andrews, Charles Boone, Zhaolei Zhang:
PhenoM: a database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae. Nucleic Acids Res. 40(Database-Issue): 687-694 (2012) - 2011
- [j4]Chao Cheng, Renqiang Min, Mark Gerstein:
TIP: A probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles. Bioinform. 27(23): 3221-3227 (2011) - 2010
- [j3]Jingjing Li, Yu Liu, TaeHyung Kim, Renqiang Min, Zhaolei Zhang:
Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility. PLoS Comput. Biol. 6(8) (2010) - 2009
- [j2]Jingjing Li, Martin Renqiang Min, Anthony J. Bonner, Zhaolei Zhang:
A Probabilistic Framework to Improve microRNA Target Prediction by Incorporating Proteomics Data. J. Bioinform. Comput. Biol. 7(6): 955-972 (2009) - [j1]Renqiang Min, Anthony J. Bonner, Jingjing Li, Zhaolei Zhang:
Learned Random-Walk Kernels and Empirical-Map Kernels for Protein Sequence Classification. J. Comput. Biol. 16(3): 457-474 (2009)
Conference and Workshop Papers
- 2024
- [c45]Xin Hu, Kai Li, Deep Patel, Erik Kruus, Martin Renqiang Min, Zhengming Ding:
Weakly-Supervised Temporal Action Localization with Multi-Modal Plateau Transformers. CVPR Workshops 2024: 2704-2713 - [c44]Kumaranage Ravindu Yasas Nagasinghe, Honglu Zhou, Malitha Gunawardhana, Martin Renqiang Min, Daniel Harari, Muhammad Haris Khan:
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos. CVPR 2024: 18816-18826 - [c43]Yuxiao Chen, Kai Li, Wentao Bao, Deep Patel, Yu Kong, Martin Renqiang Min, Dimitris N. Metaxas:
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-pathway Text-Video Alignment. ECCV (82) 2024: 193-210 - 2023
- [c42]Changhao Shi, Haomiao Ni, Kai Li, Shaobo Han, Mingfu Liang, Martin Renqiang Min:
Exploring Compositional Visual Generation with Latent Classifier Guidance. CVPR Workshops 2023: 853-862 - [c41]Kai Li, Deep Patel, Erik Kruus, Martin Renqiang Min:
Source-Free Video Domain Adaptation with Spatial-Temporal-Historical Consistency Learning. CVPR 2023: 14643-14652 - [c40]Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang, Martin Renqiang Min:
Conditional Image-to-Video Generation with Latent Flow Diffusion Models. CVPR 2023: 18444-18455 - [c39]Haifeng Xia, Kai Li, Martin Renqiang Min, Zhengming Ding:
Few-Shot Video Classification via Representation Fusion and Promotion Learning. ICCV 2023: 19254-19263 - [c38]Tianxiao Li, Hongyu Guo, Filippo Grazioli, Mark Gerstein, Martin Renqiang Min:
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering. NeurIPS 2023 - [c37]Ziqi Chen, Martin Renqiang Min, Hongyu Guo, Chao Cheng, Trevor Clancy, Xia Ning:
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy. RECOMB 2023: 174-191 - 2022
- [c36]Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu:
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis. CVPR 2022: 18176-18186 - [c35]Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas:
Learning Transferable Reward for Query Object Localization with Policy Adaptation. ICLR 2022 - [c34]Yiren Jian, Erik Kruus, Martin Renqiang Min:
T-Cell Receptor-Peptide Interaction Prediction with Physical Model Augmented Pseudo-Labeling. KDD 2022: 3090-3097 - [c33]Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, Dimitris N. Metaxas:
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders. WACV 2022: 955-964 - 2021
- [c32]Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu:
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning. EMNLP (Findings) 2021: 1990-2000 - [c31]Yao Li, Martin Renqiang Min, Thomas C. M. Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh:
Towards Robustness of Deep Neural Networks via Regularization. ICCV 2021: 7476-7485 - [c30]Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris N. Metaxas:
Dual Projection Generative Adversarial Networks for Conditional Image Generation. ICCV 2021: 14418-14427 - [c29]Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang:
Disentangled Recurrent Wasserstein Autoencoder. ICLR 2021 - [c28]Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf:
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning. ICLR 2021 - 2020
- [c27]Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin:
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance. ACL 2020: 7530-7541 - [c26]Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf:
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation. CVPR 2020: 6537-6546 - 2019
- [c25]Kai Li, Martin Renqiang Min, Bing Bai, Yun Fu, Hans Peter Graf:
On Novel Object Recognition: A Unified Framework for Discriminability and Adaptability. CIKM 2019: 2265-2268 - [c24]Kai Li, Martin Renqiang Min, Yun Fu:
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective. ICCV 2019: 3582-3591 - [c23]Yogesh Balaji, Martin Renqiang Min, Bing Bai, Rama Chellappa, Hans Peter Graf:
Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis. IJCAI 2019: 1995-2001 - [c22]Xiaoyuan Liang, Martin Renqiang Min, Hongyu Guo, Guiling Wang:
Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval. IJCAI 2019: 2966-2972 - [c21]Xiaoyuan Liang, Guiling Wang, Martin Renqiang Min, Yi Qi, Zhu Han:
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction. SDM 2019: 100-108 - 2018
- [c20]Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin:
Video Generation From Text. AAAI 2018: 7065-7072 - [c19]Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin:
Adaptive Feature Abstraction for Translating Video to Text. AAAI 2018: 7284-7291 - [c18]Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin:
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms. ACL (1) 2018: 440-450 - [c17]Dinghan Shen, Martin Renqiang Min, Yitong Li, Lawrence Carin:
Learning Context-Aware Convolutional Filters for Text Processing. EMNLP 2018: 1839-1848 - [c16]Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Dae-ki Cho, Haifeng Chen:
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. ICLR (Poster) 2018 - [c15]Ting Chen, Martin Renqiang Min, Yizhou Sun:
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations. ICML 2018: 853-862 - [c14]Yang Gao, Jeff M. Phillips, Yan Zheng, Renqiang Min, P. Thomas Fletcher, Guido Gerig:
Fully convolutional structured LSTM networks for joint 4D medical image segmentation. ISBI 2018: 1104-1108 - [c13]Martin Renqiang Min, Hongyu Guo, Dinghan Shen:
Parametric t-Distributed Stochastic Exemplar-Centered Embedding. ECML/PKDD (1) 2018: 477-493 - 2017
- [c12]Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin:
Adaptive Feature Abstraction for Translating Video to Language. ICLR (Workshop) 2017 - [c11]Martin Renqiang Min, Hongyu Guo, Dongjin Song:
Exemplar-centered Supervised Shallow Parametric Data Embedding. IJCAI 2017: 2479-2485 - [c10]Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav:
A Context-aware Attention Network for Interactive Question Answering. KDD 2017: 927-935 - 2016
- [c9]Ke Zhang, Jianwu Xu, Martin Renqiang Min, Guofei Jiang, Konstantinos Pelechrinis, Hui Zhang:
Automated IT system failure prediction: A deep learning approach. IEEE BigData 2016: 1291-1300 - 2014
- [c8]Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein:
Interpretable Sparse High-Order Boltzmann Machines. AISTATS 2014: 614-622 - [c7]Sanjay Purushotham, Martin Renqiang Min, C.-C. Jay Kuo, Rachel Ostroff:
Factorized sparse learning models with interpretable high order feature interactions. KDD 2014: 552-561 - [c6]Martin Renqiang Min, Salim A. Chowdhury, Yanjun Qi, Alex Stewart, Rachel Ostroff:
An Integrated Approach To Blood-Based Cancer Diagnosis And Biomarker Discovery. Pacific Symposium on Biocomputing 2014: 87-98 - [c5]Hao Wu, Martin Renqiang Min, Bing Bai:
Deep Semantic Embedding. SMIR@SIGIR 2014: 46-52 - 2010
- [c4]Martin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
Deep Supervised t-Distributed Embedding. ICML 2010: 791-798 - 2009
- [c3]Martin Renqiang Min, David A. Stanley, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
A Deep Non-linear Feature Mapping for Large-Margin kNN Classification. ICDM 2009: 357-366 - [c2]Martin Renqiang Min, Rui Kuang, Anthony J. Bonner, Zhaolei Zhang:
Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery. SDM 2009: 133-144 - 2007
- [c1]Martin Renqiang Min, Anthony J. Bonner, Zhaolei Zhang:
Modifying kernels using label information improves SVM classification performance. ICMLA 2007: 13-18
Informal and Other Publications
- 2024
- [i35]Kumaranage Ravindu Yasas Nagasinghe, Honglu Zhou, Malitha Gunawardhana, Martin Renqiang Min, Daniel Harari, Muhammad Haris Khan:
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos. CoRR abs/2403.02782 (2024) - [i34]Yao Wei, Martin Renqiang Min, George Vosselman, Li Erran Li, Michael Ying Yang:
Compositional 3D Scene Synthesis with Scene Graph Guided Layout-Shape Generation. CoRR abs/2403.12848 (2024) - [i33]Yuxiao Chen, Kai Li, Wentao Bao, Deep Patel, Yu Kong, Martin Renqiang Min, Dimitris N. Metaxas:
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment. CoRR abs/2409.16145 (2024) - 2023
- [i32]Yuren Cong, Martin Renqiang Min, Li Erran Li, Bodo Rosenhahn, Michael Ying Yang:
Attribute-Centric Compositional Text-to-Image Generation. CoRR abs/2301.01413 (2023) - [i31]Ziqi Chen, Martin Renqiang Min, Hongyu Guo, Chao Cheng, Trevor Clancy, Xia Ning:
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy. CoRR abs/2303.02162 (2023) - [i30]Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang, Martin Renqiang Min:
Conditional Image-to-Video Generation with Latent Flow Diffusion Models. CoRR abs/2303.13744 (2023) - [i29]Changhao Shi, Haomiao Ni, Kai Li, Shaobo Han, Mingfu Liang, Martin Renqiang Min:
Exploring Compositional Visual Generation with Latent Classifier Guidance. CoRR abs/2304.12536 (2023) - 2022
- [i28]Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas:
Learning Transferable Reward for Query Object Localization with Policy Adaptation. CoRR abs/2202.12403 (2022) - [i27]Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu:
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis. CoRR abs/2203.15799 (2022) - 2021
- [i26]Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang:
Disentangled Recurrent Wasserstein Autoencoder. CoRR abs/2101.07496 (2021) - [i25]Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf:
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning. CoRR abs/2103.10574 (2021) - [i24]Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris N. Metaxas:
Dual Projection Generative Adversarial Networks for Conditional Image Generation. CoRR abs/2108.09016 (2021) - [i23]Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, Dimitris N. Metaxas:
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders. CoRR abs/2110.08718 (2021) - 2020
- [i22]Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf:
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation. CoRR abs/2005.11437 (2020) - [i21]Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin:
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance. CoRR abs/2006.00693 (2020) - [i20]Ziqi Chen, Martin Renqiang Min, Xia Ning:
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction. CoRR abs/2012.02840 (2020) - [i19]Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning:
Molecule Optimization via Fragment-based Generative Models. CoRR abs/2012.04231 (2020) - 2019
- [i18]Zhenyu Duan, Martin Renqiang Min, Li Erran Li, Mingbo Cai, Yi Xu, Bingbing Ni:
Disentangled Deep Autoencoding Regularization for Robust Image Classification. CoRR abs/1902.11134 (2019) - [i17]Xiaoyuan Liang, Guiling Wang, Martin Renqiang Min, Yi Qi, Zhu Han:
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction. CoRR abs/1905.05614 (2019) - [i16]Kai Li, Martin Renqiang Min, Yun Fu:
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective. CoRR abs/1909.05995 (2019) - [i15]Bo Peng, Renqiang Min, Xia Ning:
CNN-based Dual-Chain Models for Knowledge Graph Learning. CoRR abs/1911.06910 (2019) - 2018
- [i14]Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin:
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms. CoRR abs/1805.09843 (2018) - [i13]Ting Chen, Martin Renqiang Min, Yizhou Sun:
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations. CoRR abs/1806.09464 (2018) - [i12]Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas C. M. Lee, Erik Kruus:
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding. CoRR abs/1811.07950 (2018) - 2017
- [i11]Martin Renqiang Min, Hongyu Guo, Dongjin Song:
Exemplar-Centered Supervised Shallow Parametric Data Embedding. CoRR abs/1702.06602 (2017) - [i10]Dinghan Shen, Martin Renqiang Min, Yitong Li, Lawrence Carin:
Adaptive Convolutional Filter Generation for Natural Language Understanding. CoRR abs/1709.08294 (2017) - [i9]Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin:
Video Generation From Text. CoRR abs/1710.00421 (2017) - [i8]Martin Renqiang Min, Hongyu Guo, Dinghan Shen:
Parametric t-Distributed Stochastic Exemplar-centered Embedding. CoRR abs/1710.05128 (2017) - [i7]Ting Chen, Martin Renqiang Min, Yizhou Sun:
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations. CoRR abs/1711.03067 (2017) - 2016
- [i6]Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat T. Chakradhar:
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent. CoRR abs/1603.05544 (2016) - [i5]Martin Renqiang Min, Hongyu Guo, Dongjin Song:
A Shallow High-Order Parametric Approach to Data Visualization and Compression. CoRR abs/1608.04689 (2016) - [i4]Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin:
Adaptive Feature Abstraction for Translating Video to Language. CoRR abs/1611.07837 (2016) - [i3]Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav:
A Context-aware Attention Network for Interactive Question Answering. CoRR abs/1612.07411 (2016) - 2015
- [i2]Hongyu Guo, Xiaodan Zhu, Martin Renqiang Min:
A Deep Learning Model for Structured Outputs with High-order Interaction. CoRR abs/1504.08022 (2015) - 2009
- [i1]Martin Renqiang Min, David A. Stanley, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
Large-Margin kNN Classification Using a Deep Encoder Network. CoRR abs/0906.1814 (2009)
Coauthor Index
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