Your search
Topic
Results
19 resources-
Murfet, D. (n.d.). Algebra and Artificial Intelligence.
-
Murfet, D. (n.d.). Mathematics of AlphaGo.
-
Siegelmann, H. T., & Sontag, E. D. (1995). On the Computational Power of Neural Nets. Journal of Computer and System Sciences, 50(1), 132–150. https://doi.org/10/dvwtc3
-
Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/
-
Wermuth, N., & Cox, D. R. (2001). Graphical Models: Overview. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 6379–6386). Oxford: Pergamon. https://doi.org/10.1016/B0-08-043076-7/00440-X
-
Fages, F., Calzone, L., Chabrier-Rivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8
-
Poggio, T. (2013). Tomaso A. Poggio autobiography (p. 54). Retrieved from http://poggio-lab.mit.edu/sites/default/files/cv/tomasopoggio.pdf
-
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative Adversarial Networks. ArXiv:1406.2661 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1406.2661
-
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401
-
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
-
Paul, A., & Venkatasubramanian, S. (2015). Why does Deep Learning work? - A perspective from Group Theory. ArXiv:1412.6621 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6621
-
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1412.6572
-
Olah, C., & Carter, S. (2016). Attention and Augmented Recurrent Neural Networks. Distill, 1(9), e1. https://doi.org/10/gf33sg
-
Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial examples in the physical world. ArXiv:1607.02533 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1607.02533
-
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2017). Understanding deep learning requires rethinking generalization. ArXiv:1611.03530 [Cs]. Retrieved from http://arxiv.org/abs/1611.03530
-
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: a survey. ArXiv:1502.05767 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1502.05767
-
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., … Song, D. (2018). Robust Physical-World Attacks on Deep Learning Models. ArXiv:1707.08945 [Cs]. Retrieved from http://arxiv.org/abs/1707.08945
-
Brown, T. B., Mané, D., Roy, A., Abadi, M., & Gilmer, J. (2018). Adversarial Patch. ArXiv:1712.09665 [Cs]. Retrieved from http://arxiv.org/abs/1712.09665
-
Winn, J. M. (2019). Model-Based Machine Learning. Taylor & Francis Incorporated.
Explore
BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
- Biology (1)
DIFFERENTIAL CALCULUS
- Differentiation (1)
MACHINE LEARNING
- Machine Learning (19)
MODEL CHECKING AND STATE MACHINES
- Symbolic logic (1)
- Transition systems (2)
PROBABILITY & STATISTICS
PROGRAMMING LANGUAGES
Methodology
- Compendium (1)
- Implementation (2)
- Sketchy (1)
Topic
- Abstract machines (2)
- Adversarial attacks (5)
- Algebra (1)
- Automatic differentiation (1)
- Bayesian inference (2)
- Bayesianism (2)
- Biology (1)
- Classical ML
- Compendium (1)
- Differentiation (1)
- Implementation (2)
- Machine learning (18)
- Probabilistic programming (1)
- Sketchy (1)
- Symbolic logic (1)
- Systems biology (1)
Resource type
- Book (1)
- Book Section (2)
- Conference Paper (1)
- Journal Article (13)
- Presentation (2)
Publication year
- Between 1900 and 1999 (2)
- Between 2000 and 2022 (15)
- Unknown (2)
