The DOI matches its Crossref record.
Learning Deep Architectures for AI
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 201–213 of 213
Automation of Legal Precedents Retrieval: Findings from a Literature Review
Mentzingen, H., António, N., & Bacao, F. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/6660983 · Cited by 16
Quantum Information and Computation for Chemistry
Olson, J., Cao, Y., Romero, J., Johnson, P., Dallaire-Demers, P.-L., Sawaya, N., Narang, P., Kivlichan, I., Wasielewski, M., & Aspuru-Guzik, A. (2017) · arXiv · doi:10.48550/arxiv.1706.05413 · Cited by 15
“More recently, a general purpose algorithm inspired by classical autoencoders (Bengio, 2009) was proposed for the compression of quantum information.”
BackgroundConnected and Automated Vehicles
Peng, H. (2016) · Mechanical Engineering · doi:10.1115/1.2016-dec-2 · Cited by 15
Vessel Classification through Convolutional Neural Networks using Passive Sonar Spectrogram Images
Cinelli, L., Chaves, G., & Lima, M. (2018) · Anais de XXXVI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · doi:10.14209/sbrt.2018.340 · Cited by 13
Suthar, A. C., Joshi, V., & Prajapati, R. (2022) · Advances in Computational Intelligence and Robotics · doi:10.4018/978-1-7998-8786-7.ch003 · Cited by 11
An Overview of Technological Revolution in Satellite Image Analysis
Department of Computer Science & Engineering, K. A. U., Jenice, A. R., Raimond, K., & Department of Computer Science & Engineering, K. A. U. (2016) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.094.01 · Cited by 11
Automated invoice processing: Machine learning-based information extraction for long tail suppliers
Krieger, F., Drews, P., & Funk, B. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200285 · Cited by 11
Positioning algorithm for AGV autonomous driving platform based on artificial neural networks
Bałazy, P., Gut, P., & Knap, P. (2021) · Robotic Systems and Applications · doi:10.21595/rsa.2021.22109 · Cited by 11
The changing fortunes of pattern recognition and computer vision
Chellappa, R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.04.005 · Cited by 8
Estimation of Distribution using Population Queue based Variational Autoencoders
Bhattacharjee, S., & Gras, R. (2019) · 2019 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790077 · Cited by 7
Shigeno, S. (2017) · Diversity and Commonality in Animals · doi:10.1007/978-4-431-56469-0_19 · Cited by 6
A novel connectivity of deep convolutional neural networks
Shen, Z., & Liu, Y. (2017) · 2017 Chinese Automation Congress (CAC) · doi:10.1109/cac.2017.8244187 · Cited by 5
A Gradient-Based Algorithm to Deceive Deep Neural Networks
Xie, T., & Li, Y. (2019) · Communications in Computer and Information Science · doi:10.1007/978-3-030-36808-1_7 · Cited by 3
Metadata
- Authors
- Bengio, Y.
- Venue
- Foundations and Trends® in Machine Learning
- Publisher
- Emerald
- Published
- 2009
- Volume
- 2
- Issue
- 1
- Pages
- 1-127
- ISSN
- 1935-8237 (ISSN-L)
- 1935-8245
- OpenAlex
- W4231109964
- Cited by
- 6,938
- References
- 0
- FWCI
- 42.65
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.