\begin{thebibliography}{10}

\bibitem{Kaggle}
Kaggle diabetic retinopathy dataset, howpublished =
  {\url{https://www.kaggle.com/c/diabetic-retinopathy-detection/data}},.

\bibitem{adiwardana2020humanlike}
Daniel Adiwardana, Minh-Thang Luong, David~R. So, Jamie Hall, Noah Fiedel,
  Romal Thoppilan, Zi~Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and
  Quoc~V. Le.
\newblock Towards a human-like open-domain chatbot, 2020.

\bibitem{bharadwaj2021using}
Shubham Bharadwaj.
\newblock Using convolutional neural networks to detect compression algorithms,
  2021.

\bibitem{brutzkus2019larger}
Alon Brutzkus and Amir Globerson.
\newblock Why do larger models generalize better? a theoretical perspective via
  the xor problem.
\newblock In {\em International Conference on Machine Learning}, pages
  822--830. PMLR, 2019.

\bibitem{dutta20193d}
Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel, Aravind Subramanian,
  Chanchal Chatterjee, Sindhu Hari, Kenton Prindle, Vishal Vaddina, Andrew
  Leach, et~al.
\newblock 3d conditional generative adversarial networks to enable large-scale
  seismic image enhancement.
\newblock {\em arXiv preprint arXiv:1911.06932}, 2019.

\bibitem{goodfellow2014generative}
Ian~J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David
  Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.
\newblock Generative adversarial networks, 2014.

\bibitem{halder-etal-2020-task}
Kishaloy Halder, Alan Akbik, Josip Krapac, and Roland Vollgraf.
\newblock Task-aware representation of sentences for generic text
  classification.
\newblock In {\em Proceedings of the 28th International Conference on
  Computational Linguistics}, pages 3202--3213, Barcelona, Spain (Online),
  December 2020. International Committee on Computational Linguistics.

\bibitem{klambauer2017selfnormalizing}
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter.
\newblock Self-normalizing neural networks, 2017.

\bibitem{krishna2021hurdles}
Kalpesh Krishna, Aurko Roy, and Mohit Iyyer.
\newblock Hurdles to progress in long-form question answering, 2021.

\bibitem{marchisio2018methodology}
Alberto Marchisio, Muhammad~Abdullah Hanif, Semeen Rehman, Maurizio Martina,
  and Muhammad Shafique.
\newblock A methodology for automatic selection of activation functions to
  design hybrid deep neural networks, 2018.

\bibitem{DBLP:journals/corr/abs-1908-08681}
Diganta Misra.
\newblock Mish: {A} self regularized non-monotonic neural activation function.
\newblock {\em CoRR}, abs/1908.08681, 2019.

\bibitem{noel2021growing}
Mathew~Mithra Noel, Advait Trivedi, Praneet Dutta, et~al.
\newblock Growing cosine unit: A novel oscillatory activation function that can
  speedup training and reduce parameters in convolutional neural networks.
\newblock {\em arXiv preprint arXiv:2108.12943}, 2021.

\bibitem{noel2021biologically}
Matthew~Mithra Noel, Shubham Bharadwaj, Venkataraman Muthiah-Nakarajan, Praneet
  Dutta, and Geraldine~Bessie Amali.
\newblock Biologically inspired oscillating activation functions can bridge the
  performance gap between biological and artificial neurons.
\newblock {\em arXiv preprint arXiv:2111.04020}, 2021.

\bibitem{data6020014}
Samiksha Pachade, Prasanna Porwal, Dhanshree Thulkar, Manesh Kokare, Girish
  Deshmukh, Vivek Sahasrabuddhe, Luca Giancardo, Gwenolé Quellec, and Fabrice
  Mériaudeau.
\newblock Retinal fundus multi-disease image dataset (rfmid): A dataset for
  multi-disease detection research.
\newblock {\em Data}, 6(2), 2021.

\bibitem{papay2020dissecting}
Sean Papay, Roman Klinger, and Sebastian Padó.
\newblock Dissecting span identification tasks with performance prediction,
  2020.

\bibitem{DBLP:journals/corr/abs-1710-05941}
Prajit Ramachandran, Barret Zoph, and Quoc~V. Le.
\newblock Searching for activation functions.
\newblock {\em CoRR}, abs/1710.05941, 2017.

\bibitem{Sawarn2021}
Aman Sawarn, Shefali Srivastava, Monika Gupta, and Smriti Srivastava.
\newblock {\em BeamAtt: Generating Medical Diagnosis from Chest X-Rays Using
  Sampling-Based Intelligence}, pages 135--150.
\newblock Springer International Publishing, Cham, 2021.

\bibitem{SHARMA2018377}
Neha Sharma, Vibhor Jain, and Anju Mishra.
\newblock An analysis of convolutional neural networks for image
  classification.
\newblock {\em Procedia Computer Science}, 132:377--384, 2018.
\newblock International Conference on Computational Intelligence and Data
  Science.

\bibitem{SILVAPALACIOS20171692}
Daniel Silva-Palacios, Cèsar Ferri, and María~José Ramírez-Quintana.
\newblock Improving performance of multiclass classification by inducing class
  hierarchies.
\newblock {\em Procedia Computer Science}, 108:1692--1701, 2017.
\newblock International Conference on Computational Science, ICCS 2017, 12-14
  June 2017, Zurich, Switzerland.

\bibitem{7064375}
Rajasekar Venkatesan and Meng~Joo Er.
\newblock Multi-label classification method based on extreme learning machines.
\newblock In {\em 2014 13th International Conference on Control Automation
  Robotics Vision (ICARCV)}, pages 619--624, 2014.

\bibitem{8416973}
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria.
\newblock Recent trends in deep learning based natural language processing
  [review article].
\newblock {\em IEEE Computational Intelligence Magazine}, 13(3):55--75, 2018.

\bibitem{yu2021survey}
Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and
  Meng Jiang.
\newblock A survey of knowledge-enhanced text generation, 2021.

\bibitem{ZAITOUN2015797}
Nida~M. Zaitoun and Musbah~J. Aqel.
\newblock Survey on image segmentation techniques.
\newblock {\em Procedia Computer Science}, 65:797--806, 2015.
\newblock International Conference on Communications, management, and
  Information technology (ICCMIT'2015).

\end{thebibliography}