Publications that acknowledge TILOS (NSF CCF-2112665) support
- C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "CoRe-ECO: Concurrent Refinement of Detailed Place-and-Route for an Efficient ECO Automation", Proc. ACM/IEEE International Conference on Computer Design, October 2021.
- J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD", Proc. ACM/IEEE International Conference on Computer-Aided Design, November 2021.
- K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-Adversarial-Network-Guided Well-Aware Placement for Analog Circuits," IEEE/ACM Asian and South Pacific Design Automation Conference (ASP-DAC), January 2022.
- R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: an open-source analytical placer for large scale heterogeneous fpgas using deep-learning toolkit," IEEE/ACM Asian and South Pacific Design Automation Conference (ASP-DAC), January 2022.
- E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs", Annual Conference on Neural Information Processing Systems (NeurIPS 2021), 2021, to appear.
- N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation", Conference on Neural Information Processing Systems (NeurIPS), 2021.
- M. Hao, Y. Li, Z. Di, N. B. Gundavarapu and X. Wang, "Test-Time Personalization with a Transformer for Human Pose Estimation", Conference on Neural Information Processing Systems (NeurIPS), 2021.
- J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-Person 3D Motion Prediction with Multi-Range Transformers", Conference on Neural Information Processing Systems (NeurIPS), 2021.
- Y. Freund, Y.-A. Ma and T. Zhang, "When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint", (preprint 2021, will acknowledge NSF CCF-2112665 in publication).
- M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A Survey of Research in Machine Learning for CAD," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021. (accepted, Keynote Paper)
- A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini and D. Z. Pan, “Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives,” IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), January, 2022. (Invited Paper)
- K. Zhu, H. Chen, M. Liu and D. Z. Pan, “Automating Analog Constraint Extraction: From Heuristics to Learning,” IEEE/ACM Asia and South Pacific Design Automation Conference (ASP-DAC), January, 2022. (Invited Paper)
- W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm", Journal of Machine Learning Research (JMLR), 2021. (Link)
- V. Gandikota, A. Mazumdar and S. Pal, "Support Recovery of Sparse Signals from a Mixture of Linear Measurements", Proceedings of the 31st International Conference on Neural Information Processing Systems, 2021.
- A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback", IEEE Journal on Selected Areas in Information Theory, 2021, pp. 942-953.
- M. Belkin, "Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation", Acta Numerica, Cambridge University Press, 2021, pp. 203-248. (Link)
- M. Shan, Q. Feng, Y. Jau and N. Atanasov, "ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description", IEEE/CVF International Conference on Computer Vision (ICCV), 2021. (Link)
- T. Duong and N. Atanasov, "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control", Robotics: Science and Systems (RSS), 2021. (Link)
- Y. Lin, Z. Jiang, J. Gu, W. Li, S. Dhar, H. Ren, B. Khailany and D. Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, pp. 748-761. (Link)