ANALIZA GEOMETRIJE MANIFOLDA U AUTOENKODERIMA
Ključne reči:
Autoenkoderi, mašinsko učenje, manifoldi, interpolacija
Apstrakt
Ovaj rad posvećen je pokušaju da se razumeju i rasvetle neki od principa u radu dubokih neuronskih mreza. Konkretno, kako širina središnjeg sloja u autoenkoderu, intrinsična dimenzionalnost ulaznog skupa podataka i dužina obučavanja mreže utiču na geometriju formiranog manifolda.
Reference
[1] Lei, Na, et al. "A geometric understanding of deep learning." Engineering 6.3 (2020): 361-374.
[2] Rey, Luis A. Pérez, Vlado Menkovski, and Jacobus W. Portegies. "Diffusion variational autoencoders." arXiv preprint arXiv:1901.08991 (2019).
[3] Ansuini, Alessio, et al. "Intrinsic dimension of data representations in deep neural networks." Advances in Neural Information Processing Systems 32 (2019).
[4] Ozair, Sherjil, and Yoshua Bengio. "Deep directed generative autoencoders." arXiv preprint arXiv:1410.0630 (2014).
[5] Goldt, Sebastian, et al. "Modeling the influence of data structure on learning in neural networks: The hidden manifold model." Physical Review X 10.4 (2020): 041044.
[6] Ceruti, Claudio, et al. "DANCo: dimensionality from angle and norm concentration." arXiv preprint arXiv:1206.3881 (2012).
[7] Farahmand, Amir Massoud, Csaba Szepesvári, and Jean-Yves Audibert. "Manifold-adaptive dimension estimation." Proceedings of the 24th international conference on Machine learning. 2007.
[8] Facco, Elena, et al. "Estimating the intrinsic dimension of datasets by a minimal neighborhood information." Scientific reports 7.1 (2017): 1-8.
[9] Chollet, Francois. Deep Learning with Python. Manning Publications, 2017.
[2] Rey, Luis A. Pérez, Vlado Menkovski, and Jacobus W. Portegies. "Diffusion variational autoencoders." arXiv preprint arXiv:1901.08991 (2019).
[3] Ansuini, Alessio, et al. "Intrinsic dimension of data representations in deep neural networks." Advances in Neural Information Processing Systems 32 (2019).
[4] Ozair, Sherjil, and Yoshua Bengio. "Deep directed generative autoencoders." arXiv preprint arXiv:1410.0630 (2014).
[5] Goldt, Sebastian, et al. "Modeling the influence of data structure on learning in neural networks: The hidden manifold model." Physical Review X 10.4 (2020): 041044.
[6] Ceruti, Claudio, et al. "DANCo: dimensionality from angle and norm concentration." arXiv preprint arXiv:1206.3881 (2012).
[7] Farahmand, Amir Massoud, Csaba Szepesvári, and Jean-Yves Audibert. "Manifold-adaptive dimension estimation." Proceedings of the 24th international conference on Machine learning. 2007.
[8] Facco, Elena, et al. "Estimating the intrinsic dimension of datasets by a minimal neighborhood information." Scientific reports 7.1 (2017): 1-8.
[9] Chollet, Francois. Deep Learning with Python. Manning Publications, 2017.
Objavljeno
2024-03-04
Sekcija
Elektrotehničko i računarsko inženjerstvo
