User contributions for Shemati
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24 November 2020
- 18:2618:26, 24 November 2020 diff hist +2 Functional regularisation for continual learning with gaussian processes →Critiques
- 18:2518:25, 24 November 2020 diff hist −7 Functional regularisation for continual learning with gaussian processes →Critiques
- 18:2518:25, 24 November 2020 diff hist +13 Functional regularisation for continual learning with gaussian processes →Critiques
- 18:2418:24, 24 November 2020 diff hist −1 Functional regularisation for continual learning with gaussian processes →Critiques
- 18:2318:23, 24 November 2020 diff hist −15 Functional regularisation for continual learning with gaussian processes →Critiques
- 18:2218:22, 24 November 2020 diff hist +2,466 Functional regularisation for continual learning with gaussian processes No edit summary
- 16:5216:52, 24 November 2020 diff hist +267 Functional regularisation for continual learning with gaussian processes →Methods
21 November 2020
- 01:0901:09, 21 November 2020 diff hist +565 When Does Self-Supervision Improve Few-Shot Learning? →Critiques
19 November 2020
- 17:3117:31, 19 November 2020 diff hist +418 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Critiques
- 17:2317:23, 19 November 2020 diff hist −14 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Navier-Stokes with Pressure
- 17:2317:23, 19 November 2020 diff hist +4 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Example
- 17:2217:22, 19 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
- 17:2117:21, 19 November 2020 diff hist −3 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Models
17 November 2020
- 23:1423:14, 17 November 2020 diff hist +678 orthogonal gradient descent for continual learning →Review
- 23:0023:00, 17 November 2020 diff hist −3 orthogonal gradient descent for continual learning →Review
- 22:5822:58, 17 November 2020 diff hist 0 orthogonal gradient descent for continual learning →Results
- 22:4722:47, 17 November 2020 diff hist +4 orthogonal gradient descent for continual learning →Introduction
- 19:2119:21, 17 November 2020 diff hist +69 stat940F21 →Paper presentation
16 November 2020
- 23:4723:47, 16 November 2020 diff hist +134 stat940F21 →Paper presentation
- 23:4523:45, 16 November 2020 diff hist −126 stat940F21 No edit summary