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Nan Ding 0002
Person information
- affiliation: Google Research, Venice, CA, USA
- affiliation (PhD 2013): Purdue University, Department of Computer Science, West Lafayette, IN, USA
- affiliation (former): Tsinghua University, Department of Electronic Engineering, Beijing, China
Other persons with the same name
- Nan Ding — disambiguation page
- Nan Ding 0001
— Dalian University of Technology, College of Computer Science and Technology, Dalian, China
- Nan Ding 0003
- Nan Ding 0004
- Nan Ding 0005
- Nan Ding 0006
- Nan Ding 0007
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2020 – today
- 2024
- [c29]Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut:
CausalLM is not optimal for in-context learning. ICLR 2024 - 2023
- [c28]Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen, Radu Soricut:
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization. CVPR 2023: 16458-16468 - [c27]Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo:
PaLI: A Jointly-Scaled Multilingual Language-Image Model. ICLR 2023 - [i23]Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut:
CausalLM is not optimal for in-context learning. CoRR abs/2308.06912 (2023) - 2022
- [c26]Nan Ding, Xi Chen, Tomer Levinboim, Soravit Changpinyo, Radu Soricut:
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks. ECCV (34) 2022: 252-268 - [c25]Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, Radu Soricut:
All You May Need for VQA are Image Captions. NAACL-HLT 2022: 1947-1963 - [i22]Nan Ding, Xi Chen, Tomer Levinboim, Beer Changpinyo, Radu Soricut:
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks. CoRR abs/2203.05126 (2022) - [i21]Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, Radu Soricut:
All You May Need for VQA are Image Captions. CoRR abs/2205.01883 (2022) - [i20]Xi Chen, Xiao Wang
PaLI: A Jointly-Scaled Multilingual Language-Image Model. CoRR abs/2209.06794 (2022) - [i19]Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen, Radu Soricut:
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization. CoRR abs/2211.12624 (2022) - 2021
- [c24]Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut:
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. CVPR 2021: 3558-3568 - [c23]Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Févry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel:
Do Transformer Modifications Transfer Across Implementations and Applications? EMNLP (1) 2021: 5758-5773 - [c22]Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut:
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning. NeurIPS 2021: 29506-29516 - [i18]Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut:
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. CoRR abs/2102.08981 (2021) - [i17]Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Févry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel:
Do Transformer Modifications Transfer Across Implementations and Applications? CoRR abs/2102.11972 (2021) - [i16]Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut:
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning. CoRR abs/2105.14099 (2021) - 2020
- [c21]Sebastian Goodman, Nan Ding, Radu Soricut:
TeaForN: Teacher-Forcing with N-grams. EMNLP (1) 2020: 8704-8717 - [c20]Xi Chen, Nan Ding, Tomer Levinboim, Radu Soricut:
Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance. Eval4NLP 2020: 51-59 - [i15]Noam Shazeer, Zhenzhong Lan, Youlong Cheng, Nan Ding, Le Hou:
Talking-Heads Attention. CoRR abs/2003.02436 (2020) - [i14]Nan Ding, Xinjie Fan, Zhenzhong Lan, Dale Schuurmans, Radu Soricut:
Attention that does not Explain Away. CoRR abs/2009.14308 (2020) - [i13]Sebastian Goodman, Nan Ding, Radu Soricut:
TeaForN: Teacher-Forcing with N-grams. CoRR abs/2010.03494 (2020) - [i12]Xi Chen, Nan Ding, Tomer Levinboim, Radu Soricut:
Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance. CoRR abs/2010.06150 (2020)
2010 – 2019
- 2018
- [c19]Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut:
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning. ACL (1) 2018: 2556-2565 - [c18]Ye Zhang, Nan Ding, Radu Soricut:
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation. NAACL-HLT 2018: 1528-1538 - [i11]Ye Zhang, Nan Ding, Radu Soricut:
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation. CoRR abs/1804.04093 (2018) - 2017
- [c17]Nan Ding, Radu Soricut:
Cold-Start Reinforcement Learning with Softmax Policy Gradient. NIPS 2017: 2817-2826 - [i10]Nan Ding, Radu Soricut:
Cold-Start Reinforcement Learning with Softmax Policy Gradients. CoRR abs/1709.09346 (2017) - 2016
- [c16]Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin:
Stochastic Gradient MCMC with Stale Gradients. NIPS 2016: 2937-2945 - [i9]Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin:
Stochastic Gradient MCMC with Stale Gradients. CoRR abs/1610.06664 (2016) - [i8]Radu Soricut, Nan Ding:
Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors. CoRR abs/1612.04342 (2016) - [i7]Radu Soricut, Nan Ding:
Multilingual Word Embeddings using Multigraphs. CoRR abs/1612.04732 (2016) - [i6]Nan Ding, Sebastian Goodman, Fei Sha, Radu Soricut:
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. CoRR abs/1612.07833 (2016) - 2015
- [j2]Changyou Chen, Wray L. Buntine
Differential Topic Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 230-242 (2015) - [c15]Nan Ding, Jia Deng, Kevin P. Murphy, Hartmut Neven:
Probabilistic Label Relation Graphs with Ising Models. ICCV 2015: 1161-1169 - [c14]Changyou Chen, Nan Ding, Lawrence Carin:
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators. NIPS 2015: 2278-2286 - [c13]Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans:
Embedding Inference for Structured Multilabel Prediction. NIPS 2015: 3555-3563 - [i5]Nan Ding, Jia Deng, Kevin Murphy, Hartmut Neven:
Probabilistic Label Relation Graphs with Ising Models. CoRR abs/1503.01428 (2015) - [i4]Vasil S. Denchev, Nan Ding, Shin Matsushima, S. V. N. Vishwanathan, Hartmut Neven:
Totally Corrective Boosting with Cardinality Penalization. CoRR abs/1504.01446 (2015) - 2014
- [c12]Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, Hartwig Adam:
Large-Scale Object Classification Using Label Relation Graphs. ECCV (1) 2014: 48-64 - [c11]Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven:
Bayesian Sampling Using Stochastic Gradient Thermostats. NIPS 2014: 3203-3211 - [i3]Ryan Babbush, Vasil S. Denchev, Nan Ding, Sergei Isakov, Hartmut Neven:
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing. CoRR abs/1406.4203 (2014) - 2012
- [c10]Changyou Chen, Nan Ding, Wray L. Buntine:
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling. ICML 2012 - [c9]Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven:
Robust Classification with Adiabatic Quantum Optimization. ICML 2012 - [i2]Changyou Chen, Wray L. Buntine, Nan Ding:
Theory of Dependent Hierarchical Normalized Random Measures. CoRR abs/1205.4159 (2012) - [i1]Changyou Chen, Nan Ding, Wray L. Buntine:
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling. CoRR abs/1206.4671 (2012) - 2011
- [c8]Nan Ding, S. V. N. Vishwanathan, Yuan (Alan) Qi:
t-divergence Based Approximate Inference. NIPS 2011: 1494-1502 - 2010
- [c7]Nan Ding, Zhijian Ou:
Variational nonparametric Bayesian Hidden Markov Model. ICASSP 2010: 2098-2101 - [c6]Nan Ding, S. V. N. Vishwanathan:
t-logistic regression. NIPS 2010: 514-522 - [c5]Nan Ding, Yuan (Alan) Qi, Rongjing Xiang, Ian M. Molloy, Ninghui Li:
Nonparametric Bayesian Matrix Factorization by Power-EP. AISTATS 2010: 169-176
2000 – 2009
- 2008
- [j1]Nan Ding, Shude Zhou, Zengqi Sun:
Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization. J. Comput. Sci. Technol. 23(1): 35-43 (2008) - [c4]Nan Ding, Shude Zhou, Ji Xu, Zengqi Sun:
A Bayesian view on the polynomial distribution model in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation 2008: 258-264 - [c3]Nan Ding, Shude Zhou, Hao Zhang, Zengqi Sun:
Marginal probability distribution estimation in characteristic space of covariance-matrix. IEEE Congress on Evolutionary Computation 2008: 1589-1595 - [p1]Nan Ding, Shude Zhou:
Linkages Detection in Histogram-Based Estimation of Distribution Algorithm. Linkage in Evolutionary Computation 2008: 25-40 - 2007
- [c2]Nan Ding, Ji Xu, Shude Zhou, Zengqi Sun:
Reducing computational complexity of estimating multivariate histogram-based probabilistic model. IEEE Congress on Evolutionary Computation 2007: 111-118 - 2006
- [c1]Nan Ding, Shude Zhou, Zengqi Sun:
Optimizing Continuous Problems Using Estimation of Distribution Algorithm Based on Histogram Model. SEAL 2006: 545-552
Coauthor Index
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last updated on 2024-10-07 22:15 CEST by the dblp team
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