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@article{esteva2017, title = {Dermatologist-level classification of skin cancer with deep neural networks}, author = {Esteva, Andre and Kuprel, Brett and Novoa, Roberto A. and Ko, Justin and Swetter, Susan M. and Blau, Helen M. and Thrun, Sebastian}, journal = {Nature}, year = {2017}, doi = {10.1038/nature21056}, url = {https://www.nature.com/articles/nature21056}, abstract = {Skin cancer is the most common human malignancy, and dermatologists are primarily responsible for its diagnosis. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs, and tested against clinical experts. The CNN achieves performance on par with all tested experts, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists.} } @article{simonyan2015, title = {Very deep convolutional networks for large-scale image recognition}, author = {Simonyan, Karen and Zisserman, Andrew}, journal = {International Conference on Learning Representations (ICLR)}, year = {2015}, doi = {10.48550/arXiv.1409.1556}, url = {https://arxiv.org/abs/1409.1556}, abstract = {In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.} } @misc{openai2023, title = {GPT-4 technical report}, author = {OpenAI and Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and others}, year = {2023}, url = {https://arxiv.org/abs/2303.08774}, doi = {10.48550/arXiv.2303.08774}, abstract = {We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10 percent of test takers.} } @article{he2016, title = {Deep residual learning for image recognition}, author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2016}, doi = {10.1109/CVPR.2016.90}, url = {https://ieeexplore.ieee.org/document/7780459}, abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.} } @misc{tensorflow2015, title = {TensorFlow: Large-scale machine learning on heterogeneous systems}, author = {Abadi, Martin and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and others}, year = {2015}, url = {https://www.tensorflow.org/}, abstract = {TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.} }

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