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Academic References & Scientific Publications

This page consolidates the peer-reviewed papers, foundational algorithms, and academic literature referenced throughout the neural architectures, loss functions, metrics, and primitives implemented in this project.

  1. Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. In IEEE International Conference on Computer Vision (ICCV 2017), pp. 2980-2988.
    DOI: 10.1109/ICCV.2017.324 | arXiv: 1708.02002

  2. Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In IEEE Fourth International Conference on 3D Vision (3DV 2016), pp. 565-571.
    DOI: 10.1109/3DV.2016.79 | arXiv: 1606.04797

  3. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), pp. 2818-2826.
    DOI: 10.1109/CVPR.2016.308 | arXiv: 1512.00567

  4. van den Oord, A., Li, Y., & Vinyals, O. (2018). Representation Learning with Contrastive Predictive Coding. arXiv Preprint, arXiv:1807.03748.
    arXiv: 1807.03748

  5. Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), pp. 586-595.
    DOI: 10.1109/CVPR.2018.00064 | arXiv: 1801.03924

  6. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), pp. 770-778.
    DOI: 10.1109/CVPR.2016.90 | arXiv: 1512.03385

  7. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems (NeurIPS 2017), Vol. 30.
    arXiv: 1706.03762

  8. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In International Conference on Learning Representations (ICLR 2014).
    arXiv: 1312.6114

  9. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems (NeurIPS 2017), Vol. 30.
    arXiv: 1706.08500