Unsupervised learning of invariant representations
Open accessThe 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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
One way of allowing networks to have one-shot properties is I-theory, in the context of learning invariant representations for object recognition.
“One way of allowing networks to have such properties is what is described by I-theory, in the context of learning invariant representations for object recognition (Anselmi et al., 2015).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Anselmi et al., 2015
I-theory aims to show that you can invariantly and discriminatively represent objects using a single sample, even of a new class.
“The theories then aim to show that you can invariantly and discriminatively represent objects using a single sample, even of a new class (Anselmi et al., 2015)24.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Anselmi et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566 · see citing works
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694 · see citing works
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works
2013 IEEE International Conference on Computer Vision
(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,232 · see citing works
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913 · see citing works
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442 · see citing works
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
Sparsity and incoherence in compressive sampling
Candès, E. J., & Romberg, J. K. (2007) · Inverse Problems · doi:10.1088/0266-5611/23/3/008 · Cited by 2,109 · see citing works
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324 · see citing works
Mallat, S. (2011) · arXiv.org · doi:10.1002/cpa.21413 · Cited by 1,176 · see citing works
Functional specificity in the human brain: a window into the functional architecture of the mind
Kanwisher, N. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005062107 · Cited by 929 · see citing works
A feedforward architecture accounts for rapid categorization
Serre, T., Oliva, A., & Poggio, T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700622104 · Cited by 918 · see citing works
Self-citationLearning invariance from transformation sequences
Földiák, P. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.194 · Cited by 661 · cited 1× · see citing works
“Unsupervised learning is the main mechanism: for a “complex” cell to pool over several simple cells, the key is a modified Hebbian rule based on temporal association (Foldiak-type rule, [30]): cells that fire in temporal contiguity are wired together.”
BackgroundRiesenhuber, M., & Poggio, T. (2000) · Nature Neuroscience · doi:10.1038/81479 · Cited by 658 · see citing works
Self-citationFaces and objects in macaque cerebral cortex
Tsao, D. Y., Freiwald, W. A., Knutsen, T., Mandeville, J. B., & Tootell, R. B. H. (2003) · Nature Neuroscience · doi:10.1038/nn1111 · Cited by 636 · see citing works
From Understanding Computation to Understanding Neural Circuitry
Marr, D., & Poggio, T. (1976) · Defense Technical Information Center (DTIC) · Cited by 612 · see citing works
Self-citationMulticlass Object Recognition with Sparse, Localized Features
Mutch, J., & Lowe, D. G. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.200 · Cited by 454 · see citing works
Self-citationSome Theorems on Distribution Functions
Cramér, H., & Wold, H. (1936) · Journal of the London Mathematical Society · doi:10.1112/jlms/s1-11.4.290 · Cited by 275 · see citing works
Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox, D. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000579 · Cited by 248 · see citing works
Serre, T., Kouh, M., Cadieu, C. F., Knoblich, U., Kreiman, G., & Poggio, T. (2005) · Defense Technical Information Center (DTIC) · Cited by 245 · see citing works
Self-citationA hierarchical bayesian model of invariant pattern recognition in the visual cortex
Dileep George, A., & Hawkins, J. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556155 · Cited by 224 · see citing works
What and where: A Bayesian inference theory of attention
Chikkerur, S. S., Serre, T., Tan, C., & Poggio, T. (2010) · Vision Research · doi:10.1016/j.visres.2010.05.013 · Cited by 196 · see citing works
Self-citationMathematics of the Neural Response
Smale, S., Rosasco, L., Bouvrie, J., Caponnetto, A., & Poggio, T. (2009) · Foundations of Computational Mathematics · doi:10.1007/s10208-009-9049-1 · Cited by 53 · see citing works
Self-citationVideo-based descriptors for object recognition
Lee, T., & Soatto, S. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.003 · Cited by 36 · see citing works
Invariance and selectivity in the ventral visual pathway
Geman, S. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2007.01.001 · Cited by 36 · see citing works
Why The Brain Separates Face Recognition From Object Recognition
Leibo, J. Z., Mutch, J., & Poggio, T. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 35 · see citing works
Self-citationLearning invariant representations and applications to face verification
Liao, Q., Leibo, J. Z., & Poggio, T. (2013) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 34 · see citing works
Self-citationThe Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex
Leibo, J. Z., Liao, Q., Anselmi, F., & Poggio, T. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004390 · Cited by 29 · see citing works
Self-citationConstructing invariant features by averaging techniques
Schulz-Mirbach, H. (2002) · IAPR International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.1994.576950 · Cited by 28 · see citing works
Poggio, T., Mutch, J., Leibo, J. Z., Rosasco, L., & Tacchetti, A. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 25 · see citing works
Self-citationThe photosensitizing action of buckwheat (Fagopyrum esculentum)
Chick, H., & Ellinger, P. (1941) · The Journal of Physiology · doi:10.1113/jphysiol.1941.sp003935 · Cited by 23 · see citing works
Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T. (2014) · Cited by 19 · see citing works
Self-citationWord-level invariant representations from acoustic waveforms
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L., & Poggio, T. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-518 · Cited by 8 · see citing works
Self-citationSymmetric measures via moments
Koloydenko, A. A. (2008) · Bernoulli · doi:10.3150/07-bej6144 · Cited by 4 · see citing works
Phone classification by a hierarchy of invariant representation layers
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L., & Poggio, T. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-510 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- C. Zhang, S. Voinea, G. Evangelopoulos, L. Rosasco, T. Poggio, Phone classifica- (NIPS). Lake Tahoe, NV., 2013.
Cited by
Sorted by citations
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047 · see citing works
Safety Verification of Deep Neural Networks
Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63387-9_1 · Cited by 854 · see citing works
Deep-learning inversion: A next-generation seismic velocity model building method
Yang, F., & Ma, J. (2019) · Geophysics · doi:10.1190/geo2018-0249.1 · Cited by 620 · see citing works
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B., & Liao, Q. (2017) · International Journal of Automation and Computing · doi:10.1007/s11633-017-1054-2 · Cited by 559 · see citing works
Self-citationUnmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580 · see citing works
Neural scene representation and rendering
Eslami, S. M. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al. (2018) · Science · doi:10.1126/science.aar6170 · Cited by 511 · see citing works
On the Expressive Power of Deep Learning: A Tensor AnalysisPreprint
Cohen, N., Sharir, O., & Shashua, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.05009 · Cited by 312 · see citing works
When and Why Are Deep Networks Better Than Shallow Ones?
Mhaskar, H. N., Liao, Q., & Poggio, T. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10913 · Cited by 178 · see citing works
Self-citationDeep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?
Giryes, R., Sapiro, G., & Bronstein, A. M. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2546221 · Cited by 162 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Understanding Image Representations by Measuring Their Equivariance and Equivalence
Lenc, K., & Vedaldi, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1098-y · Cited by 98 · see citing works
Learning Functions: When Is Deep Better Than ShallowPreprint
Mhaskar, H. N., Liao, Q., & Poggio, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.00988 · Cited by 97 · see citing works
Self-citationVisual novelty, curiosity, and intrinsic reward in machine learning and the brain
Jaegle, A., Mehrpour, V., & Rust, N. C. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.08.004 · Cited by 54 · see citing works
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A., & Poggio, T. (2016) · Current Biology · doi:10.1016/j.cub.2016.10.015 · Cited by 51 · see citing works
Self-citationAre we done with object recognition? The iCub robot’s perspective
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., & Natale, L. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.11.001 · Cited by 41 · see citing works
Self-citationScale and translation-invariance for novel objects in human vision
Han, Y., Roig, G., Geiger, G., & Poggio, T. (2020) · Scientific Reports · doi:10.1038/s41598-019-57261-6 · Cited by 44 · see citing works
Self-citationInvariant Recognition Shapes Neural Representations of Visual Input
Tacchetti, A., Işık, L., & Poggio, T. (2018) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091517-034103 · Cited by 40 · see citing works
Self-citationA neural network walks into a lab: towards using deep nets as models for human behaviorPreprint
Ma, W. J., & Peters, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02181 · Cited by 39 · see citing works
Waves traveling over a map of visual space can ignite short-term predictions of sensory input
Benigno, G., Budzinski, R. C., Davis, Z. W., Reynolds, J. H., & Muller, L. (2023) · Nature Communications · doi:10.1038/s41467-023-39076-2 · Cited by 35 · see citing works
Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status
Zhang, C., Cheng, X., Liu, J., He, J., & Liu, G. (2018) · Journal of Control Science and Engineering · doi:10.1155/2018/8676387 · Cited by 38 · see citing works
Learning Real and Boolean Functions: When Is Deep Better Than Shallow
Mhaskar, H. N., Liao, Q., & Poggio, T. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 38 · see citing works
Self-citationSymmetry-adapted representation learning
Anselmi, F., Evangelopoulos, G., Rosasco, L., & Poggio, T. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.025 · Cited by 34 · see citing works
Self-citationOptimal features for auditory categorization
Liu, S. T., Montes-Lourido, P., Wang, X., & Sadagopan, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09115-y · Cited by 30 · see citing works
Cao, L., Ramamohanarao, K., Sun, F., Li, H., Huang, W., & Aye, Z. M. M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10412 · Cited by 28 · see citing works
I-theory on depth vs width: hierarchical function composition
Poggio, T., Anselmi, F., & Rosasco, L. (2015) · CINECA IRIS Institutial Research Information System (University of Genoa) · Cited by 26 · see citing works
Self-citationEnhancing Convolutional Neural Network using Hu’s Moments
Aburass, S., Huneiti, A., & Al-Zoubi, M. B. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111216 · Cited by 26 · see citing works
Rani, S. V. J., Ioannou, I. I., Nagaradjane, P., Christophorou, C., Vassiliou, V., Yarramsetti, H., Shridhar, S., Balaji, L., & Pitsillides, A. (2023) · IEEE Access · doi:10.1109/access.2023.3308036 · Cited by 23 · see citing works
Statistical learning theory of structured data
Pastore, M., Rotondo, P., Erba, V., & Gherardi, M. (2020) · Physical review. E · doi:10.1103/physreve.102.032119 · Cited by 23 · see citing works
Theory I: Deep networks and the curse of dimensionality
Poggio, T., & Liao, Q. (2018) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpas.2018.125924 · Cited by 22 · see citing works
Self-citationClassification of Transformed and Geometrically Distorted Images using Convolutional Neural Network
Aburass, S., Huneiti, A., & Al-Zoubi, M. B. (2022) · Journal of Computer Science · doi:10.3844/jcssp.2022.757.769 · Cited by 21 · see citing works
Counting the learnable functions of geometrically structured data
Rotondo, P., Lagomarsino, M. C., & Gherardi, M. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023169 · Cited by 20 · see citing works
Theoretical Issues in Deep Networks: Approximation, Optimization and GeneralizationPreprint
Poggio, T., Banburski, A., & Liao, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09375 · Cited by 20 · see citing works
Self-citationRepresentation Learning in Sensory Cortex: A Theory
Anselmi, F., & Poggio, T. (2022) · IEEE Access · doi:10.1109/access.2022.3208603 · Cited by 20 · see citing works
Self-citationSaliency-Aware Class-Agnostic Food Image Segmentation
Yarlagadda, S. K., Montserrat, D. M., Güera, D., Boushey, C. J., Kerr, D. A., & Zhu, F. M. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3440274 · Cited by 17 · see citing works
Hu, Y., Zhang, S., Chen, X., & He, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10790 · Cited by 17 · see citing works
Invariant recognition drives neural representations of action sequences
Tacchetti, A., Işık, L., & Poggio, T. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005859 · Cited by 16 · see citing works
Self-citationNotes on Hierarchical Splines, DCLNs and i-theory
Poggio, T., Rosasco, L., Shashua, A., Cohen, N., & Anselmi, F. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 16 · see citing works
Self-citationBreast Cancer Classification Using Equivariance Transition in Group Convolutional Neural Networks
Sani, Z., Prasad, R., & Hashim, E. K. M. (2023) · IEEE Access · doi:10.1109/access.2023.3253640 · Cited by 15 · see citing works
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision
Chen, F. X., Roig, G., Işık, L., Boix, X., & Poggio, T. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 15 · see citing works
Self-citationLearning Compressed Transforms with Low Displacement Rank
Thomas, A., Gu, A., Dao, T., Atri Rudra, B., & Ré, C. (2018) · PubMed · doi:10.48550/arxiv.1810.02309 · Cited by 15 · see citing works
How deep convolutional neural networks lose spatial information with training
Tomasini, U. M., Petrini, L., Cagnetta, F., & Wyart, M. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad092c · Cited by 13 · see citing works
Dose–response prediction for in-vitro drug combination datasets: a probabilistic approach
Rønneberg, L., Kirk, P., & Zucknick, M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05256-6 · Cited by 12 · see citing works
Fundamental Limits and Tradeoffs in Invariant Representation LearningPreprint
Zhao, H., Chen, D., Aragam, B., Jaakkola, T., Gordon, G. J., & Ravikumar, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10713 · Cited by 10 · see citing works
Pridham, G., Oladosu, O., & Zhang, Y. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.07.007 · Cited by 8 · see citing works
Primary Cognitive Categories Are Determined by Their Invariances
Gärdenfors, P. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.584017 · Cited by 7 · see citing works
Srivastava, M., & Kalanit Grill-spector, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.13128 · Cited by 7 · see citing works
Evaluation of Methods for Protein Representation Learning: A Quantitative AnalysisPreprint
Ünsal, S., Ataş, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.28.359828 · Cited by 7 · see citing works
Emergence of Lie Symmetries in Functional Architectures Learned by CNNs
Bertoni, F., Montobbio, N., Sarti, A., & Citti, G. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.694505 · Cited by 6 · see citing works
The complexity of explaining neural networks through (group) invariants
Ensign, D., Neville, S., Paul, A., & Venkatasubramanian, S. (2019) · Theoretical Computer Science · doi:10.1016/j.tcs.2019.11.012 · Cited by 6 · see citing works
A Quaternion Deterministic Monogenic CNN Layer for Contrast Invariance
Moya-Sánchez, E. U., Xambó-Descamps, S., Salazar-Colores, S., Sánchez-Pérez, A., & Cortés, U. (2021) · SEMA SIMAI Springer series · doi:10.1007/978-3-030-74486-1_7 · Cited by 6 · see citing works
Tacchetti, A., Işık, L., & Poggio, T. (2016) · arXiv (Cornell University) · Cited by 6 · see citing works
Self-citationTranslation Insensitive CNNsPreprint
Sundaramoorthi, G., & Wang, T. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11238 · Cited by 6 · see citing works
Object Based Attention Through Internal GatingPreprint
Lei, J., Benjamin, A. S., & Kording, K. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04540 · Cited by 6 · see citing works
Data Symmetries and Learning in Fully Connected Neural Networks
Anselmi, F., Manzoni, L., d’Onofrio, A., Rodríguez, Á., Caravagna, G., Bortolussi, L., & Cairoli, F. (2023) · IEEE Access · doi:10.1109/access.2023.3274938 · Cited by 5 · see citing works
Self-citationSymmetry Enhanced Network Architecture Search for Complex Metasurface Design
Zhang, T., Kee, C. Y., Ang, Y. S., Li, E., & Ang, L. K. (2022) · IEEE Access · doi:10.1109/access.2022.3190419 · Cited by 5 · see citing works
Neurally plausible mechanisms for learning selective and invariant representations
Anselmi, F., Patel, A., & Rosasco, L. (2020) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-020-00088-7 · Cited by 5 · see citing works
Self-citationModelling the Perception of Colour Patterns in Vertebrates with HMAXPreprint
Renoult, J. P., Guyl, B., Mendelson, T. C., Percher, A. M., Dorignac, J., Geniet, F., & Molino, F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/552307 · Cited by 5 · see citing works
The neural architecture and developmental course of face processing
Bayet, L., & Nelson, C. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814411-4.00020-2 · Cited by 3 · see citing works
Predictive coding algorithms induce brain-like responses in artificial neural networks
Gütlin, D., & Auksztulewicz, R. (2025) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000076 · Cited by 3 · see citing works
A Simple Strategy to Provable Invariance via Orbit Mapping
Gandikota, K. V., Geiping, J., Lähner, Z., Czapliński, A., & Möller, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26348-4_23 · Cited by 2 · see citing works
Learning using statistical invariants: when they work and when they don’t
Liu, Y., Fokoué, E., & Krutz, D. E. (2025) · Machine Learning · doi:10.1007/s10994-025-06789-y · Cited by 1 · see citing works
Visual Features and Their Own Optical Flow
Betti, A., Boccignone, G., Faggi, L., Gori, M., & Melacci, S. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.768516 · Cited by 1 · see citing works
Trading robust representations for sample complexity through self-supervised visual experience
Tacchetti, A., Voinea, S., & Evangelopoulos, G. (2018) · neural information processing systems · Cited by 1 · see citing works
Self-citationDecoding Stacked Denoising Autoencoders.Preprint
Sonoda, S., & Murata, N. (2016) · arXiv (Cornell University) · Cited by 1 · see citing works
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
Poggio, T., Mhaskar, H. N., Rosasco, L., Miranda, B., & Liao, Q. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1 · see citing works
Self-citationMeasure, Manifold, Learning, and Optimization: A Theory Of Neural NetworksPreprint
Li, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12783 · Cited by 1 · see citing works
On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural NetworksPreprint
Yu, B., Zhang, J., & Zhu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10157 · Cited by 1 · see citing works
Invariant Tensor Feature Coding.Preprint
Mukuta, Y., & Harada, T. (2019) · arXiv (Cornell University) · Cited by 1 · see citing works
Equivariant neuronal populations enable simultaneous tuning and invariancePreprint
Hoeller, J., Zhong, L., Heinrich, L., Saalfeld, S., Pachitariu, M., & Romani, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.02.606398 · Cited by 1 · see citing works
Spatio-temporal convolutional networks explain neural representations of human actionsPreprint
Tacchetti, A., Işık, L., & Poggio, T. (2016) · arXiv (Cornell University)
Self-citationAutomatic Discoveries of Physical and Semantic Concepts via Association Priors of Neuron GroupsPreprint
Li, S., Jia, K., & Wang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.09438
Improved privacy of dynamic group services
Veugen, T., Doumen, J. M., Erkin, Z., Pellegrino, G., Verwer, S., & Weber, J. H. (2017) · EURASIP Journal on Information Security · doi:10.1186/s13635-017-0054-7
Discriminate-and-Rectify Encoders: Learning from Image Transformation SetsPreprint
Tacchetti, A., Voinea, S., & Evangelopoulos, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04775
Self-citationTransformational Sparse CodingPreprint
Gklezakos, D. C., & Rao, R. P. N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.03257
Differential Similarity in Higher Dimensional Spaces: Theory and ApplicationsPreprint
McCarty, L. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.03667 · Cited by 1 · see citing works
Learning Spatially Structured Image Transformations Using Planar Neural NetworksPreprint
Michelson, J., Palmer, J. H., Dasari, A., & Kunda, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01553
Does the Brain Infer Invariance Transformations From Graph Symmetries?
Linde, H. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3281406
Raman, R. K., & Varshney, L. R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108616799.010
Dose-response prediction for in-vitro drug combination datasets: a probabilistic approachPreprint
Rønneberg, L., Kirk, P., & Zucknick, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1465185/v1
Implicit Computation and the Origins of KnowledgePreprint
Grace, R. C., Morton, N. J., Grice, M., Wilson, A. C., & Kemp, S. (2021) · doi:10.31234/osf.io/b7r3n
Dose-response prediction for in-vitro drug combination datasets: a probabilistic approachPreprint
Rønneberg, L., Kirk, P., & Zucknick, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1465185/v2
Raj, R., & Yu, C. R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.14.511519
Weight Perturbations for Simulating Virtual Lesions in a Convolutional Neural Network
MacInnes, W. J., Alexandrovna, Z. N., & Feurra, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67285-9_16
Rønneberg, L., Kirk, P., & Zucknick, M. (2024) · Figshare · doi:10.6084/m9.figshare.26584080
Learning Affine-Equivariant Proximal Operators
Savir, O., Fang, Z., & Sulam, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462950
- Authors
- Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T.
- Affiliations
- Italian Institute of Technology (IT)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Poggio, Tomaso
- Venue
- Theoretical Computer Science
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 633
- Pages
- 112-121
- ISSN
- 0304-3975 (ISSN-L)
- OpenAlex
- W937846259
- Cited by
- 88
- References
- 41
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 9 · 2022: 6 · 2021: 7 · 2020: 12 · 2019: 13 · 2018: 11 · 2017: 8 · 2016: 13 · 2015: 3
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1480 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Theoretical Computer Science (publishedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- NSF (CCF-1231216)
- National Science Foundation (NSF-0640097, NSF-0827427)
- AFSOR-THRL (FA8650-05-C-7262)
- Eugene McDermott Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- unsupervised learning, invariant representations, visual object recognition, group transformation, image patches, deep learning architectures, hierarchical representation, speech recognition
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
