Erik Nijkamp

According to our database, Erik Nijkamp authored at least 36 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

XGen-7B Technical Report.

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

CoRR, 2023

CodeGen2: Lessons for Training LLMs on Programming and Natural Languages.

Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, Yingbo Zhou

CoRR, 2023

Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization.

Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp, Baharan Mirzasoleiman

CoRR, 2023

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Long Document Summarization with Top-down and Bottom-up Inference.

Bo Pang, Erik Nijkamp, Wojciech Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

2022

BigIssue: A Realistic Bug Localization Benchmark.

Paul Kassianik, Erik Nijkamp, Bo Pang, Yingbo Zhou, Caiming Xiong

CoRR, 2022

ProGen2: Exploring the Boundaries of Protein Language Models.

Erik Nijkamp, Jeffrey A. Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani

CoRR, 2022

A Conversational Paradigm for Program Synthesis.

Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong

CoRR, 2022

Learning Probabilistic Models from Generator Latent Spaces with Hat EBM.

Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC.

Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

SCRIPT: Self-Critic PreTraining of Transformers.

Erik Nijkamp, Bo Pang, Ying Nian Wu, Caiming Xiong

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Generative Text Modeling through Short Run Inference.

Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

2020

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling.

Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu

CoRR, 2020

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC.

Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

CoRR, 2020

Learning Latent Space Energy-Based Prior Model.

Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference.

Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

Proceedings of the Computer Vision - ECCV 2020, 2020

Flow Contrastive Estimation of Energy-Based Models.

Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model.

Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation.

Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, Kewei Tu

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models.

Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Learning Deep Generative Models with Short Run Inference Dynamics.

Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

CoRR, 2019

Representation Learning: A Statistical Perspective.

Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

CoRR, 2019

A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks.

Lior Deutsch, Erik Nijkamp, Yu Yang

CoRR, 2019

On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model.

Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

CoRR, 2019

Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model.

Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model.

Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model.

Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

CoRR, 2018

Building a Telescope to Look Into High-Dimensional Image Spaces.

Mitch Hill, Erik Nijkamp, Song-Chun Zhu

CoRR, 2018

2017

Generative Hierarchical Learning of Sparse FRAME Models.

Jianwen Xie, Yifei Xu, Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2011

Large-scale matrix factorization with distributed stochastic gradient descent.

Rainer Gemulla, Erik Nijkamp, Peter J. Haas, Yannis Sismanis

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

MapReduce and PACT - Comparing Data Parallel Programming Models.

Alexander Alexandrov, Stephan Ewen, Max Heimel, Fabian Hueske, Odej Kao, Volker Markl, Erik Nijkamp, Daniel Warneke

Proceedings of the Datenbanksysteme für Business, 2011

2010

BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs.

Heasoo Hwang, Andrey Balmin, Berthold Reinwald, Erik Nijkamp

IEEE Trans. Knowl. Data Eng., 2010

Massively Parallel Data Analysis with PACTs on Nephele.

Alexander Alexandrov, Dominic Battré, Stephan Ewen, Max Heimel, Fabian Hueske, Odej Kao, Volker Markl, Erik Nijkamp, Daniel Warneke

Proc. VLDB Endow., 2010

2009

Embedded Analytics in Front Office Applications.

Martin A. Oberhofer, Erik Nijkamp

Proceedings of the Datenbanksysteme in Business, 2009

Value Demonstration of Embedded Analytics for Front Office Applications.

Erik Nijkamp, Martin A. Oberhofer, Albert Maier

Proceedings of the Datenbanksysteme in Business, 2009

2008

DBPubs: multidimensional exploration of database publications.

Akanksha Baid, Andrey Balmin, Heasoo Hwang, Erik Nijkamp, Jun Rao, Berthold Reinwald, Alkis Simitsis, Yannis Sismanis, Frank van Ham

Proc. VLDB Endow., 2008