thesis.bbl

\begin{thebibliography}{}

\bibitem[Abadi et~al., 2016]{abadi2016}
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M.,
  Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R.,
  Moore, S., Murray, D.~G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P.,
  Wicke, M., Yu, Y., and Zheng, X. (2016).
\newblock Tensorflow: A system for large-scale machine learning.
\newblock In {\em 12th USENIX Symposium on Operating Systems Design and
  Implementation (OSDI 16)}, pages 265--283.

\bibitem[Anthony, 2001]{anthony2001}
Anthony, M. (2001).
\newblock {\em Discrete Mathematics of Neural Networks}.
\newblock Society for Industrial and Applied Mathematics.

\bibitem[Brock et~al., 2017]{brock2017}
Brock, A., Lim, T., Ritchie, J.~M., and Weston, N. (2017).
\newblock {SMASH:} one-shot model architecture search through hypernetworks.
\newblock {\em CoRR}, abs/1708.05344.

\bibitem[Brown et~al., 2020]{brown2020}
Brown, T.~B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P.,
  Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S.,
  Herbert{-}Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A.,
  Ziegler, D.~M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin,
  M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A.,
  Sutskever, I., and Amodei, D. (2020).
\newblock Language models are few-shot learners.
\newblock {\em CoRR}, abs/2005.14165.

\bibitem[Cai et~al., 2018]{cai2018}
Cai, H., Zhu, L., and Han, S. (2018).
\newblock Proxylessnas: Direct neural architecture search on target task and
  hardware.
\newblock {\em CoRR}, abs/1812.00332.

\bibitem[Chen et~al., 2021]{chen2021}
Chen, W., Gong, X., and Wang, Z. (2021).
\newblock Neural architecture search on {ImageNet} in four {GPU} hours: {A}
  theoretically inspired perspective.
\newblock {\em CoRR}, abs/2102.11535.

\bibitem[Deng et~al., 2009]{deng2009}
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009).
\newblock {ImageNet}: A large-scale hierarchical image database.
\newblock {\em IEEE Conference on Computer Vision and Pattern Recognition}.

\bibitem[DeVries and Taylor, 2017]{devries2017}
DeVries, T. and Taylor, G.~W. (2017).
\newblock Improved regularization of convolutional neural networks with
  {Cutout}.
\newblock {\em CoRR}, abs/1708.04552.

\bibitem[Dong and Yang, 2020]{dong2020}
Dong, X. and Yang, Y. (2020).
\newblock Nas-bench-201: Extending the scope of reproducible neural
  architecture search.
\newblock In {\em International Conference on Learning Representations (ICLR)}.

\bibitem[Dwaraknath, 2019]{dwaraknath2014}
Dwaraknath, R.~V. (2019).
\newblock Understanding the neural tangent kernel.
\newblock \url{https://rajatvd.github.io/NTK/}.
\newblock Accessed: 2021-07-30.

\bibitem[{European Space Agency}, 2022]{Sentinel}
{European Space Agency} (2022).
\newblock Sentinel online.
\newblock \url{https://sentinel.esa.int/web/sentinel/sentinel-data-access}.
\newblock Accessed: 2022-08-15.

\bibitem[Geada and McGough, 2022]{geada2022}
Geada, R. and McGough, A.~S. (2022).
\newblock {SpiderNet}: Hybrid differentiable-evolutionary architecture search
  via train-free metrics.
\newblock {\em CVPR-NAS 2022}.

\bibitem[Geada et~al., 2020]{geada2020}
Geada, R., Prangle, D., and McGough, A.~S. (2020).
\newblock {Bonsai-Net}: One-shot neural architecture search via differentiable
  pruners.
\newblock {\em CVPR-NAS 2020}.

\bibitem[Glorot et~al., 2011]{glorot2011}
Glorot, X., Bordes, A., and Bengio, Y. (2011).
\newblock Deep sparse rectifier neural networks.
\newblock {\em Proceedings of Machine Learning Research}, 15:315--323.

\bibitem[Goodfellow et~al., 2016]{goodfellow2016}
Goodfellow, I., Bengio, Y., and Courville, A. (2016).
\newblock {\em Deep Learning}.
\newblock MIT Press.

\bibitem[He et~al., 2015]{he2015}
He, K., Zhang, X., Ren, S., and Sun, J. (2015).
\newblock Deep residual learning for image recognition.
\newblock {\em ICLR}.

\bibitem[Huang et~al., 2018]{huang2018}
Huang, Y., Cheng, Y., Chen, D., Lee, H., Ngiam, J., Le, Q.~V., and Chen, Z.
  (2018).
\newblock Gpipe: Efficient training of giant neural networks using pipeline
  parallelism.
\newblock {\em CoRR}, abs/1811.06965.

\bibitem[Ioffe and Szegedy, 2015]{ioffe2015}
Ioffe, S. and Szegedy, C. (2015).
\newblock Batch normalization: Accelerating deep network training by reducing
  internal covariate shift.
\newblock {\em CoRR}, abs/1502.03167.

\bibitem[{Isabella Steward Gardner Museum}, 2022]{ISG}
{Isabella Steward Gardner Museum} (2022).
\newblock Isabella steward gardner music collection.
\newblock \url{https://www.gardnermuseum.org/experience/music}.
\newblock Accessed: 2022-08-12.

\bibitem[Jacot et~al., 2018]{jacot2018}
Jacot, A., Gabriel, F., and Hongler, C. (2018).
\newblock Neural tangent kernel: Convergence and generalization in neural
  networks.
\newblock {\em CoRR}, abs/1806.07572.

\bibitem[Karpathy, 2021]{KarpathySite}
Karpathy, A. (2021).
\newblock Karpathy.ai.
\newblock \url{https://karpathy.ai/}.
\newblock Accessed: 2021-12-29.

\bibitem[Kim et~al., 2019a]{kim2019v1}
Kim, J., Park, C., Jung, H.-J., and Choe, Y. (2019a).
\newblock Differentiable pruning method for neural networks.
\newblock {\em CoRR}, abs/1904.10921.

\bibitem[Kim et~al., 2019b]{kim2019v2}
Kim, J., Park, C., Jung, H.-J., and Choe, Y. (2019b).
\newblock Plug-in, trainable gate for streamlining arbitrary neural networks.
\newblock {\em CoRR}, abs/1904.10921.

\bibitem[Kingma and Ba, 2015]{kingma2015}
Kingma, D.~P. and Ba, J. (2015).
\newblock Adam: A method for stochastic optimization.
\newblock {\em ICLR}.

\bibitem[Krizhevsky, 2009]{kriv2009}
Krizhevsky, A. (2009).
\newblock Learning multiple layers of features from tiny images.
\newblock {\em University of Toronto}.

\bibitem[Krizhevsky et~al., 2012]{kriv2012}
Krizhevsky, A., Sutskever, I., and Hinton, G.~E. (2012).
\newblock {ImageNet} classification with deep convolutional neural networks.
\newblock {\em NIPS}.

\bibitem[Krogh and Hertz, 1992]{krogh1992}
Krogh, A. and Hertz, J.~A. (1992).
\newblock A simple weight decay can improve generalization.
\newblock {\em NIPS}.

\bibitem[{Lambda Labs}, 2018]{lambdaGPUBenchmark}
{Lambda Labs} (2018).
\newblock Deep learning gpu benchmarks - v100 vs 2080 ti vs 1080 ti vs titan v.
\newblock
  \url{https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-titan-v-vs-1080-ti-benchmark/}.
\newblock Accessed: 2022-08-15.

\bibitem[Larsson et~al., 2016]{larsson2016}
Larsson, G., Maire, M., and Shakhnarovich, G. (2016).
\newblock Fractalnet: Ultra-deep neural networks without residuals.
\newblock {\em CoRR}, abs/1605.07648.

\bibitem[LeCun et~al., 1989]{lecun1989}
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and
  Jackel, L. (1989).
\newblock Backpropagation applied to handwritten zip code recognition.
\newblock {\em Neural Computation}, pages 541--551.

\bibitem[LeCun et~al., 1998]{lecun1998}
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998).
\newblock Gradient-based learning applied to document recognition.

\bibitem[Li and Talwalkar, 2019]{li2019}
Li, L. and Talwalkar, A. (2019).
\newblock Random search and reproducibility for neural architecture search.
\newblock {\em CoRR}, abs/1902.07638.

\bibitem[Lin et~al., 2013]{lin2013}
Lin, M., Chen, Q., and Yan, S. (2013).
\newblock {Network In Network}.
\newblock {\em CoRR}, abs/1312.4400.

\bibitem[Liu et~al., 2017]{liu2017}
Liu, C., Zoph, B., Shlens, J., Hua, W., Li, L., Fei{-}Fei, L., Yuille, A.~L.,
  Huang, J., and Murphy, K. (2017).
\newblock Progressive neural architecture search.
\newblock {\em CoRR}, abs/1712.00559.

\bibitem[Liu et~al., 2018]{liu2018}
Liu, H., Simonyan, K., and Yang, Y. (2018).
\newblock {DARTS:} differentiable architecture search.
\newblock {\em CoRR}, abs/1806.09055.

\bibitem[Loshchilov and Hutter, 2016]{loshchilov2016}
Loshchilov, I. and Hutter, F. (2016).
\newblock {SGDR:} stochastic gradient descent with warm restarts.
\newblock {\em ICLR}, abs/1806.09055.

\bibitem[Lundberg and Lee, 2017]{lundberg2017}
Lundberg, S. and Lee, S. (2017).
\newblock A unified approach to interpreting model predictions.
\newblock {\em NIPS}, abs/1705.07874.

\bibitem[Luo et~al., 2018]{luo2019}
Luo, R., Tian, F., Qin, T., Chen, E., and Liu, T.-Y. (2018).
\newblock Neural architecture optimization.
\newblock {\em CoRR}, abs/1808.07233.

\bibitem[Maas et~al., 2013]{maas2013}
Maas, A.~L., Hannun, A.~Y., and Ng, A.~Y. (2013).
\newblock Rectifier nonlinearities improve neural network acoustic models.
\newblock In {\em {JMLR} Workshop and Conference Proceedings}, volume~30.

\bibitem[Martello and Toth, 1980]{martello1980}
Martello, S. and Toth, P. (1980).
\newblock Optimal and canonical solutions of the change making problem.
\newblock {\em European Journal of Operation Research}, 4.

\bibitem[{Microsoft Azure}, 2021]{AzurePricing}
{Microsoft Azure} (2021).
\newblock Linux virtual machines pricing.
\newblock
  \url{https://azure.microsoft.com/en-gb/pricing/details/virtual-machines/linux/}.
\newblock Accessed: 2021-02-26.

\bibitem[Miller et~al., 1995]{miller1995}
Miller, B.~L., Miller, B.~L., Goldberg, D.~E., and Goldberg, D.~E. (1995).
\newblock Genetic algorithms, tournament selection, and the effects of noise.
\newblock {\em Complex Systems}, 9:193--212.

\bibitem[{NVidia}, 2021]{CUDASite}
{NVidia} (2021).
\newblock Cuda zone.
\newblock \url{https://developer.nvidia.com/cuda-zone}.
\newblock Accessed: 2021-09-13.

\bibitem[Pascanu et~al., 2013]{pascanu2013}
Pascanu, R., Montufar, G., and Bengio, Y. (2013).
\newblock On the number of response regions of deep feed forward networks with
  piece-wise linear activations.
\newblock {\em CoRR}, abs/1312.6098.

\bibitem[Paszke et~al., 2019]{paszke2019}
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
  T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., K{\"{o}}pf, A., Yang,
  E.~Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B.,
  Fang, L., Bai, J., and Chintala, S. (2019).
\newblock Pytorch: An imperative style, high-performance deep learning library.
\newblock {\em CoRR}, abs/1912.01703.

\bibitem[Pham et~al., 2018]{pham2018}
Pham, H., Guan, M.~Y., Zoph, B., Le, Q.~V., and Dean, J. (2018).
\newblock Efficient neural architecture search via parameter sharing.
\newblock {\em CoRR}, abs/1802.03268.

\bibitem[{PyTorch}, 2021]{TorchSite}
{PyTorch} (2021).
\newblock {PyTorch}.
\newblock \url{https://pytorch.org/}.
\newblock Accessed: 2021-06-21.

\bibitem[Qian, 1999]{qian1999}
Qian, N. (1999).
\newblock On the momentum term in gradient descent learning algorithms.
\newblock {\em Neural Network Society}.

\bibitem[Real et~al., 2018]{real2018}
Real, E., Aggarwal, A., Huang, Y., and Le, Q.~V. (2018).
\newblock Regularized evolution for image classifier architecture search.
\newblock {\em CoRR}, abs/1802.01548.

\bibitem[Real et~al., 2017]{real2017}
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.~L., Le, Q.~V., and
  Kurakin, A. (2017).
\newblock Large-scale evolution of image classifiers.
\newblock {\em CoRR}, abs/1312.4400.

\bibitem[Ren et~al., 2020]{ren2020}
Ren, P., Xiao, Y., Chang, X., Huang, P., Li, Z., Chen, X., and Wang, X. (2020).
\newblock A comprehensive survey of neural architecture search: Challenges and
  solutions.
\newblock {\em CoRR}, abs/2006.02903.

\bibitem[Russakovsky et~al., 2014]{deng2014}
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z.,
  Karpathy, A., Khosla, A., Bernstein, M., Berg, A.~C., and Fei-Fei, L. (2014).
\newblock {ImageNet} large scale visual recognition challenge.
\newblock {\em CoRR}, abs/1409.0575.

\bibitem[{Samsung R\&D Institute China}, 2021]{samsung2021}
{Samsung R\&D Institute China} (2021).
\newblock {SRC-B} won the 1st place in {CVPR-NAS} 2021 {Competition}.

\bibitem[Silver et~al., 2018]{silver2018}
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A.,
  Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan,
  K., and Hassabis, D. (2018).
\newblock A general reinforcement learning algorithm that masters chess, shogi
  and go through self-play.
\newblock {\em Science}.

\bibitem[Simonyan and Zisserman, 2015]{simon2014}
Simonyan, K. and Zisserman, A. (2015).
\newblock Very deep convolution networks for large-scale image recognition.
\newblock {\em ICLR}.

\bibitem[Smith and Le, 2017]{smith2017}
Smith, S.~L. and Le, Q.~V. (2017).
\newblock A {Bayesian} perspective on generalization and stochastic gradient
  descent.
\newblock {\em CoRR}, abs/1710.06451.

\bibitem[Srivastava et~al., 2014]{srivastava2014}
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
  R. (2014).
\newblock Dropout: A simple way to prevent neural networks from overfitting.
\newblock {\em JMLR}.

\bibitem[Stinchcombe and White, 1989]{stinchcombe1989}
Stinchcombe, M. and White, H. (1989).
\newblock Multilayer feedforward networks are universal approximators.
\newblock {\em Neural Networks}.

\bibitem[Sumbul et~al., 2019]{sumbul2019}
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V. (2019).
\newblock Bigearthnet: A large-scale benchmark archive for remote sensing image
  understanding.

\bibitem[Szegedy et~al., 2014]{szegedy2014}
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.~E., Anguelov, D., Erhan,
  D., Vanhoucke, V., and Rabinovich, A. (2014).
\newblock Going deeper with convolutions.
\newblock {\em CoRR}.

\bibitem[Tan and Le, 2019]{tan2019}
Tan, M. and Le, Q.~V. (2019).
\newblock Efficientnet: Rethinking model scaling for convolutional neural
  networks.
\newblock {\em CoRR}, abs/1905.11946.

\bibitem[Torralba et~al., 2008]{torralba2008}
Torralba, A., Fergus, R., and Freeman, W.~T. (2008).
\newblock 80 million tiny images: a large dataset for non-parametric object and
  scene recognition.
\newblock {\em IEEE Transactions on Pattern Analysis and Machine Intelligence}.

\bibitem[{UK Department for Business, Energy, and Industrial Strategy},
  2021]{UKEnergyPrice}
{UK Department for Business, Energy, and Industrial Strategy} (2021).
\newblock Quarterly energy prices, {December} 2021.
\newblock
  \url{https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1043434/Quarterly_Energy_Prices_December_2021.pdf}.
\newblock Accessed: 2021-12-30.

\bibitem[Walsh, 2020]{walsh2020}
Walsh, F. (2020).
\newblock {AI} outperforms doctors diagnosing breast cancer.
\newblock {\em BBC News}.

\bibitem[Wright, 1975]{wright1975}
Wright, J. (1975).
\newblock The change-making problem.
\newblock {\em ACM}, 22.

\bibitem[Xiao et~al., 2017]{xiao2017}
Xiao, H., Rasul, K., and Vollgraf, R. (2017).
\newblock Fashion-mnist: a novel image dataset for benchmarking machine
  learning algorithms.

\bibitem[Xie et~al., 2020]{xi2019}
Xie, S., Kirillov, A., Girshick, R., and He, K. (2020).
\newblock Exploring randomly wired neural networks for image recognition.
\newblock {\em CoRR}, abs/1904.01569.

\bibitem[Xu et~al., 2020]{xu2020}
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.-J., and amd Hongkai~Xiong, Q.~T.
  (2020).
\newblock {PC-DARTS:} partial channel connections for memory-efficient
  architecture search.
\newblock {\em CoRR}, abs/1907.05737.

\bibitem[Ye, 2018]{ye2018}
Ye, T. (2018).
\newblock Visual object detection from lifelogs using visual non-lifelog data.

\bibitem[Yu et~al., 2019]{yu2019}
Yu, K., Sciuto, C., Jaggi, M., Musat, C., and Salzman, M. (2019).
\newblock Evaluating the search phase of neural architecture search.
\newblock {\em CoRR}, abs/1902.08142.

\bibitem[Zoph and Le, 2017]{zoph2017}
Zoph, B. and Le, Q.~V. (2017).
\newblock Neural architecture search with reinforcement learning.
\newblock {\em arXiv preprint arXiv:1611.01578}.

\bibitem[Zoph et~al., 2017]{zoph_sir2017}
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.~V. (2017).
\newblock Learning transferable architectures for scalable image recognition.
\newblock {\em CoRR}, abs/1707.07012.

\end{thebibliography}