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ORCIDErik Nijkamp
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2020 – today
- 2023
[c20]Bo Pang, Erik Nijkamp, Wojciech Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong:
Long Document Summarization with Top-down and Bottom-up Inference. EACL (Findings) 2023: 1237-1254- [c19]Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong:
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. ICLR 2023 - [i21]Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp, Baharan Mirzasoleiman:
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization. CoRR abs/2302.00138 (2023) - [i20]Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, Yingbo Zhou:
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages. CoRR abs/2305.02309 (2023) - [i19]Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryscinski, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alexander R. Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong:
XGen-7B Technical Report. CoRR abs/2309.03450 (2023) - 2022
- [c18]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. ICLR 2022 - [c17]Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. NeurIPS 2022 - [i18]Bo Pang, Erik Nijkamp, Wojciech Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong:
Long Document Summarization with Top-down and Bottom-up Inference. CoRR abs/2203.07586 (2022) - [i17]Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong:
A Conversational Paradigm for Program Synthesis. CoRR abs/2203.13474 (2022) - [i16]Erik Nijkamp, Jeffrey A. Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani:
ProGen2: Exploring the Boundaries of Protein Language Models. CoRR abs/2206.13517 (2022) - [i15]Paul Kassianik, Erik Nijkamp, Bo Pang, Yingbo Zhou, Caiming Xiong:
BigIssue: A Realistic Bug Localization Benchmark. CoRR abs/2207.10739 (2022) - [i14]Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. CoRR abs/2210.16486 (2022) - 2021
- [c16]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. EACL 2021: 1156-1165 - [c15]Erik Nijkamp, Bo Pang, Ying Nian Wu, Caiming Xiong:
SCRIPT: Self-Critic PreTraining of Transformers. NAACL-HLT 2021: 5196-5202 - [i13]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. CoRR abs/2106.02513 (2021) - 2020
- [c14]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. AAAI 2020: 5272-5280 - [c13]Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, Kewei Tu:
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation. ACL 2020: 3619-3629 - [c12]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CVPR 2020: 7515-7525 - [c11]Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CVPR 2020: 7975-7984 - [c10]Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. ECCV (6) 2020: 361-378 - [c9]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. NeurIPS 2020 - [i12]Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CoRR abs/2006.06059 (2020) - [i11]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC. CoRR abs/2006.06897 (2020) - [i10]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. CoRR abs/2006.08205 (2020) - [i9]Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu:
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling. CoRR abs/2010.09359 (2020)
2010 – 2019
- 2019
- [c8]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. CVPR 2019: 8670-8679 - [c7]Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. NeurIPS 2019: 5233-5243 - [i8]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models. CoRR abs/1903.12370 (2019) - [i7]Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model. CoRR abs/1904.09770 (2019) - [i6]Lior Deutsch, Erik Nijkamp, Yu Yang:
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks. CoRR abs/1905.02898 (2019) - [i5]Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Representation Learning: A Statistical Perspective. CoRR abs/1911.11374 (2019) - [i4]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CoRR abs/1912.00589 (2019) - [i3]Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Deep Generative Models with Short Run Inference Dynamics. CoRR abs/1912.01909 (2019) - 2018
- [i2]Mitch Hill, Erik Nijkamp, Song-Chun Zhu:
Building a Telescope to Look Into High-Dimensional Image Spaces. CoRR abs/1803.01043 (2018) - [i1]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. CoRR abs/1812.10907 (2018) - 2017
- [c6]Jianwen Xie, Yifei Xu, Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu:
Generative Hierarchical Learning of Sparse FRAME Models. CVPR 2017: 1933-1941 - 2011
- [c5]Alexander Alexandrov, Stephan Ewen, Max Heimel, Fabian Hueske, Odej Kao, Volker Markl, Erik Nijkamp, Daniel Warneke:
MapReduce and PACT - Comparing Data Parallel Programming Models. BTW 2011: 25-44 - [c4]Rainer Gemulla
, Erik Nijkamp, Peter J. Haas, Yannis Sismanis:
Large-scale matrix factorization with distributed stochastic gradient descent. KDD 2011: 69-77 - 2010
- [j3]Alexander Alexandrov, Dominic Battré, Stephan Ewen, Max Heimel, Fabian Hueske, Odej Kao, Volker Markl, Erik Nijkamp, Daniel Warneke:
Massively Parallel Data Analysis with PACTs on Nephele. Proc. VLDB Endow. 3(2): 1625-1628 (2010) - [j2]Heasoo Hwang, Andrey Balmin, Berthold Reinwald, Erik Nijkamp:
BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs. IEEE Trans. Knowl. Data Eng. 22(8): 1176-1190 (2010)
2000 – 2009
- 2009
- [c3]Martin A. Oberhofer, Erik Nijkamp:
Embedded Analytics in Front Office Applications. BTW 2009: 449-459 - [c2]Erik Nijkamp, Martin A. Oberhofer, Albert Maier:
Value Demonstration of Embedded Analytics for Front Office Applications. BTW 2009: 624-627 - [c1]Heasoo Hwang, Andrey Balmin, Berthold Reinwald, Erik Nijkamp:
BinRank: Scaling Dynamic Authority-Based Search Using Materialized SubGraphs. ICDE 2009: 66-77 - 2008
- [j1]Akanksha Baid, Andrey Balmin, Heasoo Hwang, Erik Nijkamp, Jun Rao, Berthold Reinwald, Alkis Simitsis, Yannis Sismanis, Frank van Ham:
DBPubs: multidimensional exploration of database publications. Proc. VLDB Endow. 1(2): 1456-1459 (2008)
Coauthor Index
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