User contributions for Cfmeaney
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1 December 2020
- 09:3809:38, 1 December 2020 diff hist 0 CRITICAL ANALYSIS OF SELF-SUPERVISION →Conclusion
- 09:3709:37, 1 December 2020 diff hist +142 CRITICAL ANALYSIS OF SELF-SUPERVISION →Conclusion
21 November 2020
- 10:4310:43, 21 November 2020 diff hist −3 Model Agnostic Learning of Semantic Features →Critiques
- 10:4110:41, 21 November 2020 diff hist +369 Model Agnostic Learning of Semantic Features →Multi-site Brain MRI image segmentation
- 10:2110:21, 21 November 2020 diff hist +485 SuperGLUE →Design Process
16 November 2020
- 11:2611:26, 16 November 2020 diff hist +16 ALBERT: A Lite BERT for Self-supervised Learning of Language Representations →Removing dropout
15 November 2020
- 22:1422:14, 15 November 2020 diff hist +583 Dense Passage Retrieval for Open-Domain Question Answering →Main Results
- 20:0820:08, 15 November 2020 diff hist −1 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 20:0720:07, 15 November 2020 diff hist +365 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →References
- 20:0720:07, 15 November 2020 diff hist 0 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 20:0620:06, 15 November 2020 diff hist +58 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 20:0220:02, 15 November 2020 diff hist −147 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 19:5819:58, 15 November 2020 diff hist +276 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 17:4217:42, 15 November 2020 diff hist +43 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Approach
- 17:4217:42, 15 November 2020 diff hist +226 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →References
- 17:4117:41, 15 November 2020 diff hist +46 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Approach
- 17:3217:32, 15 November 2020 diff hist −1 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 17:3117:31, 15 November 2020 diff hist +5 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 17:3117:31, 15 November 2020 diff hist −1 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 17:1817:18, 15 November 2020 diff hist +403 a fair comparison of graph neural networks for graph classification →Background
- 17:0617:06, 15 November 2020 diff hist +109 a fair comparison of graph neural networks for graph classification →References
- 16:4916:49, 15 November 2020 diff hist −1 orthogonal gradient descent for continual learning →Previous Work
- 16:4916:49, 15 November 2020 diff hist +213 orthogonal gradient descent for continual learning →References
- 16:4816:48, 15 November 2020 diff hist +2 orthogonal gradient descent for continual learning →Previous Work
- 16:4816:48, 15 November 2020 diff hist +208 orthogonal gradient descent for continual learning →Previous Work
- 16:1216:12, 15 November 2020 diff hist +217 stat940F21 →Paper presentation
- 16:0616:06, 15 November 2020 diff hist +7 stat940F21 →Paper presentation
14 November 2020
- 15:4015:40, 14 November 2020 diff hist +57 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 15:1915:19, 14 November 2020 diff hist +43 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →References
- 15:1915:19, 14 November 2020 diff hist +141 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 14:5814:58, 14 November 2020 diff hist +1 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Problem Setup
- 11:5311:53, 14 November 2020 diff hist +58 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →References
- 11:5311:53, 14 November 2020 diff hist +135 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 11:4911:49, 14 November 2020 diff hist +509 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Navier-Stokes with Pressure
- 11:4411:44, 14 November 2020 diff hist +5 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Example
- 11:4311:43, 14 November 2020 diff hist +28 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Example
- 11:3911:39, 14 November 2020 diff hist +26 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Examples
- 11:3811:38, 14 November 2020 diff hist +15 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Example
- 11:3111:31, 14 November 2020 diff hist +29 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Examples
- 11:1811:18, 14 November 2020 diff hist +642 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Data-Driven Discovery of PDEs
- 11:1011:10, 14 November 2020 diff hist +35 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Models
- 11:1011:10, 14 November 2020 diff hist +1,380 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Models
- 10:3110:31, 14 November 2020 diff hist −2 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Models
- 10:3110:31, 14 November 2020 diff hist −4 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Models
- 10:2910:29, 14 November 2020 diff hist +19 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Navier-Stokes with Pressure
- 10:2910:29, 14 November 2020 diff hist +19 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Example
- 10:2910:29, 14 November 2020 diff hist +38 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Examples
- 10:2810:28, 14 November 2020 diff hist +274 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Models
- 10:2210:22, 14 November 2020 diff hist +8 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Models
- 10:1810:18, 14 November 2020 diff hist +37 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Models