stat940F21: Difference between revisions
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|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://pdf.sciencedirectassets.com/272570/1-s2.0-S0021999118X00229/1-s2.0-S0021999118307125/main.pdf Paper] || || | |Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://pdf.sciencedirectassets.com/272570/1-s2.0-S0021999118X00229/1-s2.0-S0021999118307125/main.pdf?X-Amz-Date=20201007T125457Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Signature=4f33bf8f5af610d25f0f08f638d0bf663b6079a49393754d14ea656618ee040f&X-Amz-Credential=ASIAQ3PHCVTYV433NC4S%2F20201007%2Fus-east-1%2Fs3%2Faws4_request&type=client&tid=prr-b4dbc2bb-cd3c-424d-8b43-22fc22d81524&sid=45b42873310db64eb4893370e82b35abf675gxrqa&pii=S0021999118307125&X-Amz-SignedHeaders=host&X-Amz-Security-Token=IQoJb3JpZ2luX2VjENz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIDD116qgS%2BeYQoLCSLT4IS%2FwcfEOIC%2FHSfBTJpSZfmAfAiBOM5RNxUyryi%2Ft6vJ07KbO1csAmKnj6uTOP8%2FKVAgkYyq0AwgVEAMaDDA1OTAwMzU0Njg2NSIM243Swx0BL88sGjYhKpEDXi%2BvklCLJ6ZpebuJ9b2I99qQxn5y939LI2t%2BFPf4jUIzOSdla7vIJQbBohvv60dYKETTEmHBha2qWRZd1AhvNskzPvol1mD%2FHrX2USFAng8VwRwzIR79wMahv5ZeZxhIHNuyB9buP6nWGPgxzpljpWysBahmLIgsvotMgZmyibGtSCTFhIbz%2Frrc8mDm8pB7QXCYQM3nuYnkpVjSb8NBVQIwH3TaAGKFOuRoKLeoU4nd46dRCbPq4Nd%2FstD1uhNX%2BfqAnWOYVsrJj1Su3KuAZjPnBudloiRlVeIufObuorINSmTEm8KZmh5BqD0DAHaaei7lQUIHfm%2BsYqIt7mcWnnvhAyKJqtzvdBvYR9rHvVmFbPWgtREUqlJwXb2kPYuoaaCTGvJkPSUnXU24QycOcbr29HWbZvaTStMrzNBck3ikJmyiQp0ciXzme35b6aIshO3WxJj6jKRR4ijsd4woFy5yME60CkJsUqHT1gwOEuglsw8P61RQBBP8TexQfBor%2B1iixEGLdxdRPzRMTgJZSP0w7dD2%2BwU67AHTe9p577uLdVXlKQGK9xVSPOtbJ83%2FIE3z8tlTK8CFjqKeLta0Q31vXe0DSE7quzG1%2BsM16V6xo%2BiHLEvmz7FcBF7R8cceXAb2fsmL%2BPg1bgq3MCZjh6s4bA9enZQpc%2FzM9UODnbD%2FpxMzAF2Zk%2B6tH4%2F7ISr5Ga0r2skXaejzvqHPv1nmKEoJSvfYvguSRXzFWCJAo07Te5hAduGX9ko6wDzL6RNpaO7fq%2FXpyOAXJBlooWITFjQYXtlbaG4qdoF2FP5ZY1wd104K7DEPn7sniP765zJp5Nqo%2BXmQaogNLn21vnuqESj2SoGvXQ%3D%3D&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&X-Amz-Expires=300&hash=04a28e148c7a7f9a793cf8dd707e84f82ff6867e54fb94522ce741444d144b86 Paper] || || | ||
|- | |- | ||
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]|| || | |Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]|| || | ||
Revision as of 07:55, 7 October 2020
Project Proposal
Record your contributions here [1]
Use the following notations:
P: You have written a summary/critique on the paper.
T: You had a technical contribution on a paper (excluding the paper that you present).
E: You had an editorial contribution on a paper (excluding the paper that you present).
Paper presentation
| Date | Name | Paper number | Title | Link to the paper | Link to the summary | Link to the video |
| Sep 15 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | [2] | |
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| Week of Nov 16 | 13 | |||||
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| Week of Nov 16 | 15 | |||||
| Week of Nov 16 | Cameron Meaney | 16 | Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations | Paper | ||
| Week of Nov 16 | Sobhan Hemati | 17 | Adversarial Fisher Vectors for Unsupervised Representation Learning | Paper | ||
| Week of Nov 16 | Milad Sikaroudi | 18 | Domain Genralization via Model Agnostic Learning of Semantic Features | Paper | ||
| Week of Nov 23 | Bowen You | 19 | DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION | Paper | ||
| Week of Nov 23 | Nouha Chatti | 20 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Paper | ||
| Week of Nov 23 | Mohan Wu | 21 | Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification | Paper | ||
| Week of Nov 23 | Xinyi Yan | 22 | Incorporating BERT into Neural Machine Translation | Paper | ||
| Week of Nov 23 | Meixi Chen | 23 | Functional Regularisation for Continual Learning with Gaussian Processes | Paper | ||
| Week of Nov 23 | Ahmed Salamah | 24 | Sparse Convolutional Neural Networks | Paper | ||
| Week of Nov 30 | Danial Maleki | 25 | Attention Is All You Need | Paper | ||
| Week of Nov 30 | Gursimran Singh | 26 | BERTScore: Evaluating Text Generation with BERT. | Paper | ||
| Week of Nov 30 | Govind Sharma | 27 | Time-series Generative Adversarial Networks | Paper | ||
| Week of Nov 30 | Maral Rasoolijaberi | 28 | Parameter-free, Dynamic, and Strongly-Adaptive Online Learning | Paper | ||
| Week of Nov 30 | Sina Farsangi | 29 | A Baseline for Few-Shot Image Classification | Paper | ||
| Week of Nov 30 | Pierre McWhannel | 30 | Pre-training Tasks for Embedding-based Large-scale Retrieval | Paper | placeholder |