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ORCIDAmirmohammad Rooshenas
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- affiliation: University of Illinois Chicago, IL, USA
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2020 – today
- 2024
[i8]Daniel Wesego, Amirmohammad Rooshenas:
Revising Multimodal VAEs with Diffusion Decoders. CoRR abs/2408.16883 (2024)- [i7]Sara Riazi, Pedram Rooshenas:
LLM-Driven Feedback for Enhancing Conceptual Design Learning in Database Systems Courses. CoRR abs/2412.17892 (2024) - 2023
- [c10]Akarsh Pokkunuru, Pedram Rooshenas, Thilo Strauss, Anuj Abhishek, Taufiquar Khan:
Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography. ICLR 2023 - [i6]Daniel Wesego, Amirmohammad Rooshenas:
Score-Based Multimodal Autoencoders. CoRR abs/2305.15708 (2023) - 2021
- [c9]Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum:
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. ACL/IJCNLP (1) 2021: 4528-4537 - 2020
- [c8]MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas, Phillip Wallis:
Differential Equation Units: Learning Functional Forms of Activation Functions from Data. AAAI 2020: 6030-6037 - [i5]Subhajit Naskar, Amirmohammad Rooshenas, Simeng Sun, Mohit Iyyer, Andrew McCallum:
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. CoRR abs/2009.13267 (2020)
2010 – 2019
- 2019
- [c7]Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum:
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks. NeurIPS 2019: 13522-13532 - [i4]MohamadAli Torkamani, Phillip Wallis, Shiv Shankar, Amirmohammad Rooshenas:
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions. CoRR abs/1905.07685 (2019) - [i3]MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas, Phillip Wallis:
Differential Equation Units: Learning Functional Forms of Activation Functions from Data. CoRR abs/1909.03069 (2019) - 2018
- [c6]Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum:
Training Structured Prediction Energy Networks with Indirect Supervision. NAACL-HLT (2) 2018: 130-135 - [i2]Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum:
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks. CoRR abs/1812.09603 (2018) - 2016
- [c5]Amirmohammad Rooshenas, Daniel Lowd:
Discriminative Structure Learning of Arithmetic Circuits. AAAI 2016: 4258-4259 - [c4]Amirmohammad Rooshenas, Daniel Lowd:
Discriminative Structure Learning of Arithmetic Circuits. AISTATS 2016: 1506-1514 - 2015
- [j1]Daniel Lowd, Amirmohammad Rooshenas:
The Libra toolkit for probabilistic models. J. Mach. Learn. Res. 16: 2459-2463 (2015) - [i1]Daniel Lowd, Amirmohammad Rooshenas:
The Libra Toolkit for Probabilistic Models. CoRR abs/1504.00110 (2015) - 2014
- [c3]Amirmohammad Rooshenas, Daniel Lowd:
Learning Sum-Product Networks with Direct and Indirect Variable Interactions. ICML 2014: 710-718 - 2013
- [c2]Amirmohammad Rooshenas, Daniel Lowd:
Learning Tractable Graphical Models Using Mixture of Arithmetic Circuits. AAAI (Late-Breaking Developments) 2013 - [c1]Daniel Lowd, Amirmohammad Rooshenas:
Learning Markov Networks With Arithmetic Circuits. AISTATS 2013: 406-414
Coauthor Index
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