| LDR | | 02419nmm uu200457 4500 |
| 001 | | 000000333464 |
| 005 | | 20240805173058 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438325272 |
| 035 | |
▼a (MiAaPQ)AAI10821852 |
| 035 | |
▼a (MiAaPQ)berkeley:17964 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Abbasi Asl, Reza. |
| 245 | 10 |
▼a Interpretable Machine Learning with Applications in Neuroscience. |
| 260 | |
▼a [S.l.] :
▼b University of California, Berkeley.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 104 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Bin Yu. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a In the past decade, research in machine learning has been principally focused on the development of algorithms and models with high predictive capabilities. Models such as convolutional neural networks (CNNs) achieve state-of-the-art predictive |
| 520 | |
▼a In this thesis, we investigate two regimes based on (1) compression and (2) stability to build more interpretable machine learning models. These regimes will be demonstrated in a computational neuroscience study. In the first part of the thesis, |
| 520 | |
▼a In the second part of this thesis, we introduce DeepTune, a stability-driven visualization and interpretation framework for CNN-based models. DeepTune is used to characterize biological neurons in the V4 area of the primate visual cortex. V4 is |
| 520 | |
▼a In the final part of this thesis, we study the application of CAR and RAR compressed CNNs in modeling V4 neurons. Both CAR and RAR compression give rise to a new set of simpler models for V4 neurons with similar accuracy to existing state-of-the |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Neurosciences. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0317 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Electrical Engineering & Computer Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998426
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |