User contributions for Cfmeaney
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1 December 2020
- 10:3810:38, 1 December 2020 diff hist 0 CRITICAL ANALYSIS OF SELF-SUPERVISION →Conclusion
- 10:3710:37, 1 December 2020 diff hist +142 CRITICAL ANALYSIS OF SELF-SUPERVISION →Conclusion
21 November 2020
- 11:4311:43, 21 November 2020 diff hist −3 Model Agnostic Learning of Semantic Features →Critiques
- 11:4111:41, 21 November 2020 diff hist +369 Model Agnostic Learning of Semantic Features →Multi-site Brain MRI image segmentation
- 11:2111:21, 21 November 2020 diff hist +485 SuperGLUE →Design Process
16 November 2020
- 12:2612:26, 16 November 2020 diff hist +16 ALBERT: A Lite BERT for Self-supervised Learning of Language Representations →Removing dropout
15 November 2020
- 23:1423:14, 15 November 2020 diff hist +583 Dense Passage Retrieval for Open-Domain Question Answering →Main Results
- 21:0821:08, 15 November 2020 diff hist −1 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 21:0721:07, 15 November 2020 diff hist +365 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →References
- 21:0721:07, 15 November 2020 diff hist 0 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 21:0621:06, 15 November 2020 diff hist +58 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 21:0221:02, 15 November 2020 diff hist −147 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 20:5820:58, 15 November 2020 diff hist +276 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION →Introduction
- 18:4218:42, 15 November 2020 diff hist +43 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Approach
- 18:4218:42, 15 November 2020 diff hist +226 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →References
- 18:4118:41, 15 November 2020 diff hist +46 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Approach
- 18:3218:32, 15 November 2020 diff hist −1 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 18:3118:31, 15 November 2020 diff hist +5 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 18:3118:31, 15 November 2020 diff hist −1 Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates →Introduction
- 18:1818:18, 15 November 2020 diff hist +403 a fair comparison of graph neural networks for graph classification →Background
- 18:0618:06, 15 November 2020 diff hist +109 a fair comparison of graph neural networks for graph classification →References
- 17:4917:49, 15 November 2020 diff hist −1 orthogonal gradient descent for continual learning →Previous Work
- 17:4917:49, 15 November 2020 diff hist +213 orthogonal gradient descent for continual learning →References
- 17:4817:48, 15 November 2020 diff hist +2 orthogonal gradient descent for continual learning →Previous Work
- 17:4817:48, 15 November 2020 diff hist +208 orthogonal gradient descent for continual learning →Previous Work
- 17:1217:12, 15 November 2020 diff hist +217 stat940F21 →Paper presentation
- 17:0617:06, 15 November 2020 diff hist +7 stat940F21 →Paper presentation
14 November 2020
- 16:4016:40, 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
- 16:1916:19, 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
- 16:1916:19, 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
- 15:5815:58, 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
- 12:5312:53, 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
- 12:5312:53, 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
- 12:4912:49, 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
- 12:4412:44, 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
- 12:4312:43, 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
- 12:3912:39, 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
- 12:3812:38, 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
- 12:3112:31, 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
- 12:1812:18, 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
- 12:1012:10, 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
- 12:1012:10, 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
- 11:3111:31, 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
- 11:3111:31, 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
- 11:2911:29, 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
- 11:2911:29, 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
- 11:2911:29, 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
- 11:2811:28, 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
- 11:2211:22, 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
- 11:1811:18, 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