| formalisms: |
| - id: pca |
| name: Principal Component Analysis |
| year: 1901 |
| origin: Pearson; Hotelling (1933) |
| signature: |
| operation: project |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: linear_projection |
| macro_type: eigenvalue_problem |
| canonical_reference: Pearson, K. (1901). On lines and planes of closest fit. |
| status: seed |
| - id: cca |
| name: Canonical Correlation Analysis |
| year: 1936 |
| origin: Hotelling |
| signature: |
| operation: project |
| domain: vector |
| codomain: vector |
| objective_family: correlation |
| meso_type: joint_embedding |
| macro_type: eigenvalue_problem |
| canonical_reference: Hotelling, H. (1936). Relations between two sets of variates. |
| status: seed |
| - id: kernel_cca |
| name: Kernel Canonical Correlation Analysis |
| year: 2002 |
| origin: Bach & Jordan |
| signature: |
| operation: project |
| domain: vector |
| codomain: vector |
| objective_family: correlation |
| meso_type: kernel_method |
| macro_type: eigenvalue_problem |
| canonical_reference: Bach, F.R. & Jordan, M.I. (2002). Kernel independent component analysis. JMLR. |
| status: seed |
| - id: ica |
| name: Independent Component Analysis |
| year: 1994 |
| origin: Comon |
| signature: |
| operation: decompose |
| domain: vector |
| codomain: vector |
| objective_family: information |
| meso_type: linear_projection |
| macro_type: optimization |
| canonical_reference: Comon, P. (1994). Independent component analysis, a new concept? |
| status: seed |
| - id: fisher_lda |
| name: Fisher Linear Discriminant Analysis |
| year: 1936 |
| origin: Fisher |
| signature: |
| operation: project |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: linear_projection |
| macro_type: eigenvalue_problem |
| canonical_reference: Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. |
| status: seed |
| - id: mds |
| name: Multi-Dimensional Scaling |
| year: 1952 |
| origin: Torgerson |
| signature: |
| operation: project |
| domain: matrix |
| codomain: vector |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: 'Torgerson, W.S. (1952). Multidimensional scaling: I. Theory and method.' |
| status: seed |
| - id: isomap |
| name: Isometric Feature Mapping |
| year: 2000 |
| origin: Tenenbaum, de Silva & Langford |
| signature: |
| operation: project |
| domain: manifold |
| codomain: vector |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: Tenenbaum, J.B., de Silva, V., & Langford, J.C. (2000). A global geometric framework for nonlinear |
| dimensionality reduction. Science. |
| status: seed |
| - id: lle |
| name: Locally Linear Embedding |
| year: 2000 |
| origin: Roweis & Saul |
| signature: |
| operation: project |
| domain: manifold |
| codomain: vector |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: Roweis, S.T. & Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science. |
| status: seed |
| - id: laplacian_eigenmaps |
| name: Laplacian Eigenmaps |
| year: 2003 |
| origin: Belkin & Niyogi |
| signature: |
| operation: project |
| domain: manifold |
| codomain: vector |
| objective_family: energy |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: Belkin, M. & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. |
| Neural Computation. |
| researchor_artifact_id: laplacian_matrix |
| status: seed |
| - id: svd |
| name: Singular Value Decomposition |
| year: 1873 |
| origin: Beltrami; Jordan; Golub & Reinsch |
| signature: |
| operation: decompose |
| domain: matrix |
| codomain: matrix |
| objective_family: none |
| meso_type: linear_projection |
| macro_type: eigenvalue_problem |
| canonical_reference: Golub, G.H. & Reinsch, C. (1970). Singular value decomposition and least squares solutions. |
| researchor_artifact_id: singular_value_decomposition |
| status: seed |
| - id: nmf |
| name: Non-negative Matrix Factorization |
| year: 1999 |
| origin: Lee & Seung |
| signature: |
| operation: decompose |
| domain: matrix |
| codomain: matrix |
| objective_family: divergence |
| meso_type: linear_projection |
| macro_type: optimization |
| canonical_reference: Lee, D.D. & Seung, H.S. (1999). Learning the parts of objects by non-negative matrix factorization. |
| Nature. |
| status: seed |
| canonical_arxiv_id: 0408058 |
| - id: kernel_pca |
| name: Kernel Principal Component Analysis |
| year: 1998 |
| origin: Schölkopf, Smola & Müller |
| signature: |
| operation: project |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: kernel_method |
| macro_type: eigenvalue_problem |
| canonical_reference: Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue |
| problem. Neural Computation. |
| status: seed |
| - id: kernel_ridge_regression |
| name: Kernel Ridge Regression |
| year: 1998 |
| origin: Saunders, Gammerman & Vovk; extended by many |
| signature: |
| operation: transform |
| domain: vector |
| codomain: scalar |
| objective_family: none |
| meso_type: kernel_method |
| macro_type: optimization |
| canonical_reference: Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge regression learning algorithm in dual variables. |
| ICML. |
| status: seed |
| - id: gaussian_process |
| name: Gaussian Process Regression |
| year: 1996 |
| origin: Williams & Rasmussen; roots in kriging (Matheron 1963) |
| signature: |
| operation: transform |
| domain: vector |
| codomain: distribution |
| objective_family: likelihood |
| meso_type: kernel_method |
| macro_type: statistical_inference |
| canonical_reference: Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press. |
| status: seed |
| - id: svm |
| name: Support Vector Machine |
| year: 1995 |
| origin: Cortes & Vapnik |
| signature: |
| operation: transform |
| domain: vector |
| codomain: assignment |
| objective_family: none |
| meso_type: kernel_method |
| macro_type: optimization |
| canonical_reference: Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning. |
| status: seed |
| - id: mmd |
| name: Maximum Mean Discrepancy |
| year: 2006 |
| origin: Gretton, Borgwardt, Rasch, Schölkopf & Smola |
| signature: |
| operation: match |
| domain: distribution |
| codomain: scalar |
| objective_family: none |
| meso_type: kernel_method |
| macro_type: statistical_inference |
| canonical_reference: Gretton, A. et al. (2012). A kernel two-sample test. JMLR. |
| status: seed |
| - id: kl_divergence_min |
| name: KL Divergence Minimization |
| year: 1951 |
| origin: Kullback & Leibler |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: information_geometry |
| macro_type: optimization |
| canonical_reference: Kullback, S. & Leibler, R.A. (1951). On information and sufficiency. |
| researchor_artifact_id: relative_entropy |
| status: seed |
| - id: mutual_info_max |
| name: Mutual Information Maximization |
| year: 1948 |
| origin: Shannon; McGill (1954) |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: scalar |
| objective_family: information |
| meso_type: information_geometry |
| macro_type: optimization |
| canonical_reference: Shannon, C.E. (1948). A mathematical theory of communication. |
| researchor_artifact_id: mutual_information |
| status: seed |
| - id: cross_entropy_min |
| name: Cross-Entropy Minimization |
| year: 1948 |
| origin: Shannon; Good (1956) |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: information_geometry |
| macro_type: optimization |
| canonical_reference: Good, I.J. (1956). The population frequencies of species and the estimation of population parameters. |
| researchor_artifact_id: entropy |
| status: seed |
| - id: infonce |
| name: InfoNCE / Contrastive Estimation |
| year: 2018 |
| origin: van den Oord, Li & Vinyals |
| signature: |
| operation: maximize |
| domain: vector |
| codomain: scalar |
| objective_family: information |
| meso_type: joint_embedding |
| macro_type: optimization |
| canonical_reference: van den Oord, A., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive |
| coding. arXiv:1807.03748. |
| status: seed |
| canonical_arxiv_id: '1807.03748' |
| - id: js_divergence |
| name: Jensen-Shannon Divergence |
| year: 1991 |
| origin: Lin; extended from KL |
| signature: |
| operation: match |
| domain: distribution |
| codomain: scalar |
| objective_family: divergence |
| meso_type: information_geometry |
| macro_type: statistical_inference |
| canonical_reference: Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Trans. Info. Theory. |
| status: seed |
| - id: elbo |
| name: Evidence Lower BOund Maximization |
| year: 1999 |
| origin: Jordan, Ghahramani, Jaakkola & Saul; variational Bayes |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: variational |
| macro_type: optimization |
| canonical_reference: Jordan, M.I. et al. (1999). An introduction to variational methods for graphical models. Machine Learning. |
| status: seed |
| canonical_arxiv_id: '1312.6114' |
| - id: variational_inference |
| name: Variational Inference |
| year: 1990 |
| origin: Hinton & van Camp; Jordan et al.; Wainwright & Jordan |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: variational |
| macro_type: statistical_inference |
| canonical_reference: Wainwright, M.J. & Jordan, M.I. (2008). Graphical models, exponential families, and variational inference. |
| Foundations & Trends in ML. |
| status: seed |
| - id: mean_field |
| name: Mean-Field Approximation |
| year: 1937 |
| origin: Landau; Weiss; applied to stat mech and later VI |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: mean_field |
| macro_type: statistical_inference |
| canonical_reference: Parisi, G. (1988). Statistical Field Theory. Addison-Wesley. |
| status: seed |
| - id: mle |
| name: Maximum Likelihood Estimation |
| year: 1922 |
| origin: Fisher |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: scalar |
| objective_family: likelihood |
| meso_type: probabilistic_inference |
| macro_type: statistical_inference |
| canonical_reference: Fisher, R.A. (1922). On the mathematical foundations of theoretical statistics. |
| status: seed |
| - id: map_estimation |
| name: Maximum A Posteriori Estimation |
| year: 1763 |
| origin: Bayes; Laplace |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: scalar |
| objective_family: likelihood |
| meso_type: probabilistic_inference |
| macro_type: statistical_inference |
| canonical_reference: Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis. Springer. |
| status: seed |
| - id: em_algorithm |
| name: Expectation-Maximization |
| year: 1977 |
| origin: Dempster, Laird & Rubin |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: distribution |
| objective_family: likelihood |
| meso_type: variational |
| macro_type: optimization |
| canonical_reference: Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the |
| EM algorithm. JRSS-B. |
| status: seed |
| - id: mcmc |
| name: Markov Chain Monte Carlo |
| year: 1953 |
| origin: Metropolis, Rosenbluth, Rosenbluth, Teller & Teller; Hastings (1970) |
| signature: |
| operation: sample |
| domain: distribution |
| codomain: sequence |
| objective_family: none |
| meso_type: probabilistic_inference |
| macro_type: stochastic_process |
| canonical_reference: Metropolis, N. et al. (1953). Equation of state calculations by fast computing machines. |
| researchor_artifact_id: markov_chain |
| status: seed |
| - id: empirical_bayes |
| name: Empirical Bayes / Type-II Maximum Likelihood |
| year: 1955 |
| origin: Robbins; Efron & Morris |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: distribution |
| objective_family: likelihood |
| meso_type: probabilistic_inference |
| macro_type: statistical_inference |
| canonical_reference: Robbins, H. (1955). An empirical Bayes approach to statistics. Proc. Third Berkeley Symp. |
| status: seed |
| - id: normalizing_flow |
| name: Normalizing Flow |
| year: 2015 |
| origin: Rezende & Mohamed; Dinh, Krueger & Bengio (NICE 2014) |
| signature: |
| operation: transform |
| domain: distribution |
| codomain: distribution |
| objective_family: likelihood |
| meso_type: variational |
| macro_type: statistical_inference |
| canonical_reference: Rezende, D.J. & Mohamed, S. (2015). Variational inference with normalizing flows. ICML. |
| status: seed |
| canonical_arxiv_id: '1505.05770' |
| - id: boltzmann_distribution |
| name: Boltzmann / Gibbs Distribution |
| year: 1868 |
| origin: Boltzmann; Gibbs (1902) |
| signature: |
| operation: sample |
| domain: scalar_field |
| codomain: distribution |
| objective_family: energy |
| meso_type: energy_model |
| macro_type: hamiltonian_system |
| canonical_reference: Gibbs, J.W. (1902). Elementary Principles in Statistical Mechanics. |
| researchor_mental_model_id: ergodicity |
| status: seed |
| - id: free_energy_min |
| name: Free Energy Minimization |
| year: 1873 |
| origin: Helmholtz; Gibbs |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: energy |
| meso_type: energy_model |
| macro_type: hamiltonian_system |
| canonical_reference: Helmholtz, H. (1882). Die Thermodynamik chemischer Vorgänge. |
| researchor_mental_model_id: phase_transitions |
| status: seed |
| - id: hopfield_network |
| name: Hopfield Network / Spin Glass |
| year: 1982 |
| origin: Hopfield; Sherrington & Kirkpatrick (1975 spin glass) |
| signature: |
| operation: minimize |
| domain: vector |
| codomain: assignment |
| objective_family: energy |
| meso_type: spin_system |
| macro_type: hamiltonian_system |
| canonical_reference: Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational |
| abilities. PNAS. |
| researchor_mental_model_id: attractors_and_basins |
| status: seed |
| - id: renormalization_group |
| name: Renormalization Group |
| year: 1971 |
| origin: Wilson; Kadanoff (block-spin 1966) |
| signature: |
| operation: transform |
| domain: scalar_field |
| codomain: scalar_field |
| objective_family: none |
| meso_type: energy_model |
| macro_type: hamiltonian_system |
| canonical_reference: Wilson, K.G. (1971). Renormalization group and critical phenomena. Phys. Rev. B. |
| researchor_mental_model_id: scale_invariance |
| status: seed |
| - id: langevin_dynamics |
| name: Langevin Dynamics |
| year: 1908 |
| origin: Langevin; adapted to sampling by Parisi (1981) |
| signature: |
| operation: sample |
| domain: scalar_field |
| codomain: distribution |
| objective_family: energy |
| meso_type: dynamical_system |
| macro_type: stochastic_process |
| canonical_reference: Langevin, P. (1908). Sur la théorie du mouvement brownien. |
| researchor_artifact_id: brownian_motion |
| status: seed |
| - id: hamiltonian_monte_carlo |
| name: Hamiltonian Monte Carlo |
| year: 1987 |
| origin: Duane, Kennedy, Pendleton & Roweth; Neal (2011 MCMC handbook) |
| signature: |
| operation: sample |
| domain: distribution |
| codomain: sequence |
| objective_family: energy |
| meso_type: dynamical_system |
| macro_type: hamiltonian_system |
| canonical_reference: Neal, R.M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo. |
| status: seed |
| - id: diffusion_sde |
| name: Diffusion Process / Reverse-Time SDE |
| year: 2015 |
| origin: Sohl-Dickstein et al.; Ho, Jain & Abbeel (DDPM 2020); Song et al. (SDE 2021) |
| signature: |
| operation: sample |
| domain: distribution |
| codomain: distribution |
| objective_family: none |
| meso_type: diffusion_process |
| macro_type: stochastic_process |
| canonical_reference: Song, Y. et al. (2021). Score-based generative modeling through stochastic differential equations. |
| ICLR. |
| researchor_artifact_id: brownian_motion |
| researchor_mental_model_id: irreversibility |
| status: seed |
| canonical_arxiv_id: '2011.13456' |
| - id: wasserstein_distance |
| name: Wasserstein Distance / Earth Mover's Distance |
| year: 1781 |
| origin: Monge; Kantorovich (1942) |
| signature: |
| operation: match |
| domain: distribution |
| codomain: scalar |
| objective_family: none |
| meso_type: optimal_transport |
| macro_type: optimization |
| canonical_reference: Kantorovich, L.V. (1942). On the translocation of masses. |
| status: seed |
| - id: sinkhorn_algorithm |
| name: Sinkhorn-Knopp Algorithm / Entropic OT |
| year: 1967 |
| origin: Sinkhorn & Knopp; Cuturi (2013) for ML |
| signature: |
| operation: match |
| domain: distribution |
| codomain: matrix |
| objective_family: divergence |
| meso_type: optimal_transport |
| macro_type: optimization |
| canonical_reference: 'Cuturi, M. (2013). Sinkhorn distances: lightspeed computation of optimal transport. NeurIPS.' |
| status: seed |
| canonical_arxiv_id: '1306.0895' |
| - id: kantorovich_dual |
| name: Kantorovich Duality |
| year: 1942 |
| origin: Kantorovich |
| signature: |
| operation: maximize |
| domain: distribution |
| codomain: scalar |
| objective_family: none |
| meso_type: optimal_transport |
| macro_type: optimization |
| canonical_reference: 'Villani, C. (2008). Optimal Transport: Old and New. Springer.' |
| status: seed |
| - id: gradient_descent |
| name: Gradient Descent |
| year: 1847 |
| origin: Cauchy |
| signature: |
| operation: minimize |
| domain: scalar_field |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Cauchy, A. (1847). Méthode générale pour la résolution des systèmes d'équations simultanées. |
| researchor_artifact_id: gradient_descent |
| status: seed |
| - id: sgd |
| name: Stochastic Gradient Descent |
| year: 1951 |
| origin: Robbins & Monro |
| signature: |
| operation: minimize |
| domain: scalar_field |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Robbins, H. & Monro, S. (1951). A stochastic approximation method. |
| researchor_artifact_id: stochastic_gradient_descent |
| status: seed |
| - id: lagrange_multiplier |
| name: Constrained Optimization (Lagrange Multiplier) |
| year: 1788 |
| origin: Lagrange |
| signature: |
| operation: minimize |
| domain: scalar_field |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Lagrange, J.-L. (1788). Mécanique Analytique. |
| status: seed |
| - id: proximal_gradient |
| name: Proximal Gradient Method |
| year: 2005 |
| origin: Combettes & Wajs; Beck & Teboulle (FISTA 2009) |
| signature: |
| operation: minimize |
| domain: scalar_field |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Beck, A. & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm. SIAM J. Imaging |
| Sci. |
| status: seed |
| - id: adam |
| name: Adam Optimizer |
| year: 2014 |
| origin: Kingma & Ba |
| signature: |
| operation: minimize |
| domain: scalar_field |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: 'Kingma, D.P. & Ba, J. (2015). Adam: a method for stochastic optimization. ICLR.' |
| status: seed |
| canonical_arxiv_id: '1412.6980' |
| - id: softmax_attention |
| name: Softmax Attention / Weighted Aggregation |
| year: 2014 |
| origin: Bahdanau, Cho & Bengio; Vaswani et al. (Transformer 2017) |
| signature: |
| operation: aggregate |
| domain: sequence |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: none |
| canonical_reference: Vaswani, A. et al. (2017). Attention is all you need. NeurIPS. |
| status: seed |
| canonical_arxiv_id: '1706.03762' |
| - id: residual_connection |
| name: Residual Connection / Skip Connection |
| year: 2016 |
| origin: He, Zhang, Ren & Sun; earlier in Hochreiter & Schmidhuber (LSTM 1997) |
| signature: |
| operation: transform |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: none |
| canonical_reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR. |
| status: seed |
| canonical_arxiv_id: '1512.03385' |
| - id: batch_normalization |
| name: Batch Normalization |
| year: 2015 |
| origin: Ioffe & Szegedy |
| signature: |
| operation: transform |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: none |
| canonical_reference: 'Ioffe, S. & Szegedy, C. (2015). Batch normalization: accelerating deep network training. ICML.' |
| status: seed |
| canonical_arxiv_id: '1502.03167' |
| - id: vae |
| name: Variational Autoencoder |
| year: 2013 |
| origin: Kingma & Welling; Rezende, Mohamed & Wierstra |
| signature: |
| operation: transform |
| domain: vector |
| codomain: distribution |
| objective_family: divergence |
| meso_type: variational |
| macro_type: statistical_inference |
| canonical_reference: Kingma, D.P. & Welling, M. (2014). Auto-encoding variational Bayes. ICLR. |
| status: seed |
| canonical_arxiv_id: '1312.6114' |
| - id: gan |
| name: Generative Adversarial Network |
| year: 2014 |
| origin: Goodfellow et al. |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: adversarial |
| meso_type: game_theoretic |
| macro_type: optimization |
| canonical_reference: Goodfellow, I.J. et al. (2014). Generative adversarial nets. NeurIPS. |
| researchor_mental_model_id: nash_equilibrium |
| status: seed |
| canonical_arxiv_id: '1406.2661' |
| - id: contrastive_learning |
| name: Contrastive Learning |
| year: 2005 |
| origin: Chopra, Hadsell & LeCun (Siamese, 2005); Hadsell et al. (dimensionality reduction 2006) |
| signature: |
| operation: minimize |
| domain: vector |
| codomain: scalar |
| objective_family: energy |
| meso_type: joint_embedding |
| macro_type: optimization |
| canonical_reference: Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. |
| CVPR. |
| researchor_mental_model_id: signal_vs_noise |
| status: seed |
| - id: knowledge_distillation |
| name: Knowledge Distillation |
| year: 2015 |
| origin: Hinton, Vinyals & Dean |
| signature: |
| operation: minimize |
| domain: distribution |
| codomain: distribution |
| objective_family: divergence |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. NeurIPS workshop. |
| status: seed |
| canonical_arxiv_id: '1503.02531' |
| - id: layer_normalization |
| name: Layer Normalization |
| year: 2016 |
| origin: Ba, Kiros & Hinton |
| signature: |
| operation: transform |
| domain: vector |
| codomain: vector |
| objective_family: none |
| meso_type: none |
| macro_type: none |
| canonical_reference: Ba, J.L., Kiros, J.R., & Hinton, G.E. (2016). Layer normalization. arXiv:1607.06450. |
| status: seed |
| canonical_arxiv_id: '1607.06450' |
| - id: ridge_regression |
| name: Ridge Regression (L2 Regularization) |
| year: 1970 |
| origin: Hoerl & Kennard; Tikhonov (1943) |
| signature: |
| operation: minimize |
| domain: vector |
| codomain: scalar |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: 'Hoerl, A.E. & Kennard, R.W. (1970). Ridge regression: biased estimation for nonorthogonal problems.' |
| status: seed |
| - id: lasso |
| name: LASSO (L1 Regularization) |
| year: 1996 |
| origin: Tibshirani |
| signature: |
| operation: minimize |
| domain: vector |
| codomain: scalar |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. JRSS-B. |
| status: seed |
| - id: elastic_net |
| name: Elastic Net |
| year: 2005 |
| origin: Zou & Hastie |
| signature: |
| operation: minimize |
| domain: vector |
| codomain: scalar |
| objective_family: none |
| meso_type: none |
| macro_type: optimization |
| canonical_reference: Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. JRSS-B. |
| status: seed |
| - id: spectral_clustering |
| name: Spectral Clustering |
| year: 2000 |
| origin: Shi & Malik (normalized cuts); Ng, Jordan & Weiss (2002) |
| signature: |
| operation: decompose |
| domain: graph |
| codomain: assignment |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: 'Ng, A.Y., Jordan, M.I., & Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. NeurIPS.' |
| researchor_artifact_id: laplacian_matrix |
| status: seed |
| - id: pagerank |
| name: PageRank |
| year: 1998 |
| origin: Page, Brin, Motwani & Winograd |
| signature: |
| operation: propagate |
| domain: graph |
| codomain: vector |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: eigenvalue_problem |
| canonical_reference: 'Page, L. et al. (1999). The PageRank citation ranking: bringing order to the web.' |
| researchor_artifact_id: adjacency_matrix |
| researchor_mental_model_id: network_centrality |
| status: seed |
| - id: fourier_transform |
| name: Fourier Transform / Spectral Decomposition |
| year: 1822 |
| origin: Fourier; Cooley & Tukey (FFT 1965) |
| signature: |
| operation: decompose |
| domain: sequence |
| codomain: sequence |
| objective_family: none |
| meso_type: spectral_method |
| macro_type: none |
| canonical_reference: Fourier, J.B.J. (1822). Théorie analytique de la chaleur. |
| researchor_mental_model_id: signal_vs_noise |
| status: seed |
|
|
