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Nicholas Ruozzi
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2020 – today
- 2024
- [c38]Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, Yu Xiang:
Mean Shift Mask Transformer for Unseen Object Instance Segmentation. ICRA 2024: 2760-2766 - [c37]Benjamin Rheault, Shivvrat Arya, Akshay Vyas, Jikai Wang, Rohith Peddi, Brett Benda, Vibhav Gogate, Nicholas Ruozzi, Yu Xiang, Eric D. Ragan:
Predictive Task Guidance with Artificial Intelligence in Augmented Reality. VR Workshops 2024: 973-974 - [i15]Yangxiao Lu, Jishnu Jaykumar P, Yunhui Guo, Nicholas Ruozzi, Yu Xiang:
Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation. CoRR abs/2405.17859 (2024) - 2023
- [j6]Chiradeep Roy
, Mahsan Nourani
Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models. ACM Trans. Interact. Intell. Syst. 13(4): 29:1-29:32 (2023) - [c36]Hailiang Dong, James Amato, Vibhav Gogate
A New Modeling Framework for Continuous, Sequential Domains. AISTATS 2023: 11118-11131 - [c35]Alec G. Moore, Tiffany D. Do, Nicholas Ruozzi, Ryan P. McMahan:
Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly. ISMAR 2023: 396-404 - [c34]Yangxiao Lu, Ninad A. Khargonkar, Zesheng Xu, Charles Averill, Kamalesh Palanisamy, Kaiyu Hang, Yunhui Guo, Nicholas Ruozzi, Yu Xiang:
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction. Robotics: Science and Systems 2023 - [i14]Yangxiao Lu, Ninad Khargonkar
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction. CoRR abs/2302.03793 (2023) - [i13]Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Jikai Wang, Qifan Zhang, Vasundhara Komaragiri, Eric D. Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate
CaptainCook4D: A dataset for understanding errors in procedural activities. CoRR abs/2312.14556 (2023) - 2022
- [j5]Péter Csikvári, Nicholas Ruozzi
Markov Random Fields, Homomorphism Counting, and Sidorenko's Conjecture. IEEE Trans. Inf. Theory 68(9): 6052-6062 (2022) - [c33]Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate
Conditionally Tractable Density Estimation using Neural Networks. AISTATS 2022: 6933-6946 - [c32]Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi:
Relational Neural Markov Random Fields. AISTATS 2022: 8260-8269 - [c31]Hao Xiong, Yangxiao Lu, Nicholas Ruozzi:
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs. NeurIPS 2022 - [i12]Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, Yu Xiang:
Mean Shift Mask Transformer for Unseen Object Instance Segmentation. CoRR abs/2211.11679 (2022) - 2021
- [j4]Alec G. Moore
Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario. Big Data Cogn. Comput. 5(3): 29 (2021) - [c30]Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate
Dynamic Cutset Networks. AISTATS 2021: 3106-3114 - [c29]Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi:
Personal Identifiability and Obfuscation of User Tracking Data From VR Training Sessions. ISMAR 2021: 221-228 - [c28]Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi:
Personal Identifiability of User Tracking Data During VR Training. VR Workshops 2021: 556-557 - [i11]Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi:
Relational Neural Markov Random Fields. CoRR abs/2110.09647 (2021) - 2020
- [c27]Hao Xiong, Nicholas Ruozzi:
General Purpose MRF Learning with Neural Network Potentials. IJCAI 2020: 2769-2776 - [c26]Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi:
Lifted Hybrid Variational Inference. IJCAI 2020: 4237-4244 - [c25]Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi:
Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application. ISMAR 2020: 694-703 - [i10]Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi:
Lifted Hybrid Variational Inference. CoRR abs/2001.02773 (2020) - [i9]Mahsan Nourani, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate:
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. CoRR abs/2005.02335 (2020)
2010 – 2019
- 2019
- [c24]Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi
Marginal Inference in Continuous Markov Random Fields Using Mixtures. AAAI 2019: 7834-7841 - [c23]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. DGS@ICLR 2019 - [c22]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
Correlated Variational Auto-Encoders. ICML 2019: 6135-6144 - [c21]Yuqiao Chen, Nicholas Ruozzi
Lifted Message Passing for Hybrid Probabilistic Inference. IJCAI 2019: 5701-5707 - [c20]Shahab Shams, Nicholas Ruozzi
Counting Homomorphisms in Bipartite Graphs. ISIT 2019: 1487-1491 - [c19]Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate
Explainable Activity Recognition in Videos. IUI Workshops 2019 - [c18]Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi
One-Shot Inference in Markov Random Fields. UAI 2019: 102-112 - [i8]Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. CoRR abs/1905.05335 (2019) - [i7]Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara:
Learning Correlated Latent Representations with Adaptive Priors. CoRR abs/1906.06419 (2019) - 2018
- [c17]Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi
Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. AAAI 2018: 2860-2867 - [i6]Yibo Yang, Nicholas Ruozzi, Vibhav Gogate:
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. CoRR abs/1806.05355 (2018) - 2017
- [c16]Nicholas Ruozzi:
A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case. AISTATS 2017: 1048-1056 - [c15]Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi
Efficient Inference for Untied MLNs. IJCAI 2017: 4617-4624 - [c14]Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi
Sparse Approximate Conic Hulls. NIPS 2017: 2534-2544 - 2016
- [c13]Li Chou, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate
On Parameter Tying by Quantization. AAAI 2016: 3241-3247 - [c12]Kui Tang, Nicholas Ruozzi
Bethe Learning of Graphical Models via MAP Decoding. AISTATS 2016: 1096-1104 - 2015
- [c11]Nicholas Ruozzi:
Exactness of Approximate MAP Inference in Continuous MRFs. NIPS 2015: 2332-2340 - [c10]Khaled Al-Naami, Gbadebo Ayoade, Asim Siddiqui, Nicholas Ruozzi, Latifur Khan
P2V: Effective Website Fingerprinting Using Vector Space Representations. SSCI 2015: 59-66 - [i5]Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara:
Bethe Learning of Conditional Random Fields via MAP Decoding. CoRR abs/1503.01228 (2015) - 2014
- [c9]Nicholas Ruozzi, Tony Jebara:
Making Pairwise Binary Graphical Models Attractive. NIPS 2014: 1772-1780 - 2013
- [j3]Nicholas Ruozzi
Message-passing algorithms for quadratic minimization. J. Mach. Learn. Res. 14(1): 2287-2314 (2013) - [j2]Nicholas Ruozzi
Message-Passing Algorithms: Reparameterizations and Splittings. IEEE Trans. Inf. Theory 59(9): 5860-5881 (2013) - [c8]Nicholas Ruozzi:
Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function. UAI 2013 - [i4]Nicholas Ruozzi:
Beyond Log-Supermodularity: Lower Bounds and the Bethe Partition Function. CoRR abs/1309.6859 (2013) - 2012
- [c7]Nicholas Ruozzi:
Convergent message-passing algorithms in the presence of erasures. Allerton Conference 2012: 1566-1571 - [c6]Nicholas Ruozzi
The Bethe Partition Function of Log-supermodular Graphical Models. NIPS 2012: 117-125 - [i3]Nicholas Ruozzi:
The Bethe Partition Function of Log-supermodular Graphical Models. CoRR abs/1202.6035 (2012) - [i2]Nicholas Ruozzi, Sekhar Tatikonda:
Message-Passing Algorithms for Quadratic Minimization. CoRR abs/1212.0171 (2012) - 2010
- [c5]Nicholas Ruozzi, Sekhar Tatikonda:
Unconstrained minimization of quadratic functions via min-sum. CISS 2010: 1-5 - [c4]Nicholas Ruozzi, Sekhar Tatikonda:
Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models. UAI 2010: 500 - [i1]Nicholas Ruozzi, Sekhar Tatikonda:
Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models. CoRR abs/1002.3239 (2010)
2000 – 2009
- 2009
- [j1]Dexter Kozen, Nicholas Ruozzi:
Applications of Metric Coinduction. Log. Methods Comput. Sci. 5(3) (2009) - [c3]Nicholas Ruozzi, Justin Thaler, Sekhar Tatikonda:
Graph covers and quadratic minimization. Allerton 2009: 1590-1596 - 2008
- [c2]Nicholas Ruozzi, Sekhar Tatikonda:
s-t paths using the min-sum algorithm. Allerton 2008: 918-921 - 2007
- [c1]Dexter Kozen, Nicholas Ruozzi:
Applications of Metric Coinduction. CALCO 2007: 327-341
Coauthor Index
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last updated on 2024-08-20 20:30 CEST by the dblp team
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