CO542 Neural Networks Reading Group (E15) : Department of Computer Engineering, University of Peradeniya

CO542 Neural Networks Reading Group (E15)

Department of Computer Engineering, Faculty of Engineering, University of Peradeniya

This reading group was organized parallel to the CO542 neural networks and fuzzy logic undergraduate course for E15 in (early) 2021.

The next iteration for E16 undergraduates held in (late) 2021 is available on https://cepdnaclk.github.io/co542-neural-networks-reading-group-e16/.

Student feedback
Schedule
Papers
Questions
Groups and memebers
Instructions
Booklet submission template
Marks

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Feedback from students and analysis

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Schedule

The discussion group takes place every friday 4:00pm (IST) onwards

Click here to add the schedule to your google calender.

Week	Date	Papers	Zoom link
Week 1	2021 Jan 29	1	Recording
Week 2	2021 Feb 05	2, 5, 12	Recording
Week 3	2021 Feb 12	3, 13	Recording
Week 4	2021 Feb 19	16, 9, 11
Week 5		8, 15	Asynchronous
Week 6		17, 18	Asynchronous
Week 7		6, 4	Asynchronous

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Papers

eng.pdn.ac.lk email users can download the papers from here.

Paper No.	Category	Paper	Discussion	Group	Slides
1	Math: activations	He, K., Zhang, X., Ren, S. and Sun, J., 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).	Discussion on: week 1	10	Slides Youtube Book
2	Math: Optimizers	Kingma, D.P. and Ba, J.,. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, (ICLR) 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings	Discussion on: week 2	14	Slides Youtube Book
3	Math: Backprop	Rumelhart, D.E., Hinton, G.E. and Williams, R.J., 1986. Learning representations by back-propagating errors. nature, 323(6088), pp.533-536.	Discussion on: week 3	5	Slides Youtube Book
4	NN: DNN	Srivastava, R.K., Greff, K. and Schmidhuber, J., 2015. Training very deep networks. Advances in neural information processing systems, 28, pp.2377-2385.	Discussion on: week 7	15	Slides Youtube Book
5	NN: CNN	LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278-2324.	Discussion on: week 2	7	Slides Youtube Book
6	NN: LSTM	Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780.	Discussion on: week 7	1	Slides Youtube Book
7	NN: Autoencoders	Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A. and Bottou, L., 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12).
8	NN: GAN	Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., 2014. Generative adversarial nets. Advances in neural information processing systems, 27, pp.2672-2680.	Discussion on: week 5	3	Slides Youtube Book
9	NN: Edge computing	Kim, Y.D., Park, E., Yoo, S., Choi, T., Yang, L. and Shin, D., 2015. Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530.	Discussion on: week 4	9	Slides Youtube Book
10	NN: GPU	Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Desmaison, A. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems (pp. 8026-8037).
11	App: image detection	Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), pp.84-90.	Discussion on: week 5	13	Slides Youtube Book
12	App: video	Wojke, N., Bewley, A. and Paulus, D., 2017, September. Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP) (pp. 3645-3649). IEEE.
13	App: NLP	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).	Discussion on: week 3	4	Slides Youtube Book
14	App: Style transfer	Gatys, L.A., Ecker, A.S. and Bethge, M., 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2414-2423).
15	App: VQA	Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C. and Parikh, D., 2015. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (pp. 2425-2433).	Discussion on: week 6	2	Slides Youtube Book
16	NN: uncertainity	Gal, Y. and Ghahramani, Z., 2016, June. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059).	Discussion on: week 4	6	Slides Youtube Book
17	NN: datasets	This group will present a summary about the datasets used in the papers 1-16.	Discussion on: week 6	11	Slides Youtube Book
18	NN: loss functions	This group will present a summary about the loss functions used in the papers 1-16	Discussion on: week 6	12	Slides Youtube Book
19	App: games	~~Brown, N. and Sandholm, T., 2017. Safe and nested subgame solving for imperfect-information games. In Advances in neural information processing systems (pp. 689-699).~~
20	NN: interpretation	~~Zeiler, M.D. and Fergus, R., 2014, September. Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833). Springer, Cham.~~

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Questions for groups

The list of questions for the groups to answer are here.

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Group members

Google sheet. Paper assignment is done in random. If you REALLY want to know how it was done, read this.

Group Number	Memeber 1	Memeber 2	Memeber 3	Memeber 4	Paper No.
1	E/15/073: Ayesh	E/15/315: Thisum	E/15/281: Dinelka	E/15/181: Tharindu	6
2	E/15/187: Devin	E/15/092: Imesh	E/15/043: Yasiru	E/15/246: Rajitha	15
3	E/15/016: Anojan	E/15/351: Thakshajini	E/15/171: Kapilrajh	E/15/348: Suhail	8
4	E/15/373: Vaheesan	E/15/366: Thinesh	E/15/330: Sathursan	E/15/021: Aslam	13
5	E/15/209: Kithma	E/15/233: Nipuni	E/15/325: Chalani	E/15/220: Hashan	3
6	E/15/142: Ganindu	E/15/154: Chamin	E/15/179: Anandi	E/15/350: Pasan	16
7	E/15/279: Wathsari	E/15/077: Kshithija	E/15/211: Ishani	E/15/076: Sandushi	5
8	~~E/15/287: Rajinth~~	~~E/15/121: Chamika~~	~~E/15/109: Harindu~~	~~E/15/241: Dinuka~~	12
9	E/15/065: Prasad	E/15/119: Dinithi	E/15/202: Dulanjali	E/15/208: Roshani	9
10	E/15/238: Sewwandie	E/15/362: Hasini	E/15/345: Vidwa	E/15/081: Imalsha	1
11	E/15/280: Pubudu	E/15/316: Suneth	E/15/123: Wishma	E/15/173: Dilshani	17
12	E/15/271: Sonali	E/15/243: Sewwandi	E/15/048: Laksara	E/15/363: Rashmi	18
13	E/15/129: Pasindu	E/15/106: Dhanushka	E/15/310: Sachinthaka	E/15/260: Thilina	11
14	E/15/299: Malitha	E/15/139: Sajini	E/15/249: Dasuni	E/15/292: Madushi	2
15	E/15/369: Dilan	E/13/087: Lochana	E/15/258: Isuru	E/15/178: Chrishni	4

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Instructions

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Booklet submission template

You have to use this latex template.
If you are new to overleaf/latex, there are enough online tutorials on the matter. Learning to use latex properly will be very important in the long run for a few of you. Since we don't know who that few is, we are expecting all of you to learn it.

If you are a ME student struggling with overleaf/latex, ask a CO student (who has already submitted their 7th sem FYP report in Latex).

If you are a CO student who is comfortable in Latex, try to dig deep into who invented Tex (Donald Knuth), download his masterpiece TAOCP and skim through a few pages. This might motivate a few of you to look at computing in a whole new light. Since we don't know who that few is, give it a try!

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Marks

The final marks are here.

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Last updated: 2021 May 20