From machine learning to machine reasoning

Ahmed, A., Yu, K., Xu, W., Gong, Y., & Xing, E. P. (2008). Training hierarchical feed-forward visual recognition models using transfer learning from pseudo tasks. In Proc. 10th European conference on computer vision (ECCV).

Aiello, M., Pratt-Hartmann, I., & van Benthem, J. (Eds.) (2007). Handbook of spatial logics. Berlin: Springer.

Bakır, G. H., Hofmann, T., Schölkopf, B., Smola, A. J., Taskar, B., & Vishwanathan, S. V. N. (Eds.) (2007). Predicting structured data. Cambridge: MIT Press.

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems (Vol. 19, pp. 153–160). Cambridge: MIT Press.

Bordes, A., Weston, J., Collobert, R., & Bengio, Y. (2011). Learning structured embeddings of knowledge bases. In Proc. 25th conference on artificial intelligence (AAAI).

Bottou, L. (2008). Artificial intelligence in seven years? Seminar presentation, University of Montreal, June 2008. http://www.iro.umontreal.ca/~lisa/seminaires/26-06-2008.html .

Bottou, L. (2011). From machine learning to machine reasoning, February 2011. arXiv:1102.1808v3 .

Bottou, L., & Gallinari, P. (1991). A framework for the cooperation of learning algorithms. In Advances in neural information processing systems (Vol. 3). San Mateo: Morgan Kaufmann.

Bottou, L., LeCun, Y., & Bengio, Y. (1997). Global training of document processing systems using graph transformer networks. In Proc. of computer vision and pattern recognition (pp. 489–493). New York: IEEE Press.

Buntine, W. (1994). Operations for learning with graphical models. The Journal of Artificial Intelligence Research, 2, 159–225.

Caruana, R. (1997). Multitask learning. Machine Learning, 28, 41–75.

Collobert, R. (2011). Deep learning for efficient discriminative parsing. In Proc. artificial intelligence and statistics (AISTAT).

Collobert, R., & Weston, J. (2007). Fast semantic extraction using a novel neural network architecture. In Proc. 45th annual meeting of the association of computational linguistics (ACL) (pp. 560–567).

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12, 2493–2537.

Wiesel, T. N., & Hubel, D. H. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160, 106–154.

Etter, V. (2009). Semantic vector machines. Master’s thesis, EPFL.

Friedman, N., Getoor, L., Koller, D., & Pfeffer, A. (1999). Learning probabilistic relational models. In Proc. sixteenth international joint conference on artificial intelligence (pp. 1300–1307).

Grangier, D., Bottou, L., & Collobert, R. (2009). Deep convolutional networks for scene parsing. ICML 2009 Deep Learning Workshop. http://david.grangier.info/scene_parsing .

Harris, Z. S. (1968). Mathematical structures of language. New York: Wiley.

Hilbert, D., & Ackermann, W. (1928). Grundzüge der theoretischen Logik. Berlin: Springer.

Hinton, G. E. (1990). Mapping part-whole hierarchies into connectionist networks. Artificial Intelligence, 46, 47–75.

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.

Hoiem, D., Stein, A., Efros, A. A., & Hebert, M. (2007). Recovering occlusion boundaries from a single image. In Proc. international conference on computer vision (CVPR).

Khardon, R., & Roth, D. (1997). Learning to reason. Journal of the ACM, 44(5), 697–725.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

LeCun, Y., Bottou, L., & HuangFu, J. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In Proc. computer vision and pattern recognition.

Lighthill, J. (1973). Artificial intelligence: a general survey. In Artificial intelligence: a paper symposium. Science Research Council.

Lonardi, S., Sperduti, A., & Starita, A. (1994). Encoding pyramids by labeling RAAM. In Proc. neural networks for signal processing.

Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 10–60.

Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 343–355.

Miller, M. (2006). Personal communication.

Minsky, M., & Papert, S. (1969). Perceptrons. Cambridge: MIT Press.

Neville, J., & Jensen, D. (2003). Collective classification with relational dependency networks. In Proc. second international workshop on multi-relational data mining (pp. 77–91).

NIPS (1987–2010). Advances in neural processing information systems. Volumes 0 to 22.

Paccanaro, A., & Hinton, G. E. (2001). Learning hierarchical structures with linear relational embedding. In Advances in neural information processing systems (Vol. 14). Cambridge: MIT Press.

Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo: Morgan Kaufmann.

Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge: Cambridge University Press.

Piaget, J. (1937). La construction du réel chez l’enfant. Neuchatel: Delachaux et Niestlé.

Plate, T. (1994). Distributed Representations and Nested Compositional Structure. PhD thesis, Department of Computer Science, University of Toronto.

Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77–105.

Popper, K. (1959). The logic of scientific discovery. Stroudsburg: Dowden, Hutchinson and Ross.

Richardson, M., & Domingos, P. (2006). Markov logic networks. Journal of Machine Learning Research, 62, 107–136.

Riesenhuber, M., & Poggio, T. (2003). How visual cortex recognizes objects: the tale of the standard model. The Visual Neurosciences, 2, 1640–1653.

Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Communications of the ACM, 5, 23–41.

Roth, D. (1996). On the hardness of approximate reasoning. Artificial Intelligence, 82, 273–302.

Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008). Labelme: a database and web-based tool for image annotation. International Journal of Computer Vision, 77(1–3), 157–173.

Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159–216.

Socher, R., Ng, A., & Manning, C. (2010). Learning continuous phrase representations and syntactic parsing with recursive neural networks. NIPS Deep Learning workshop presentation, November 2010. http://deeplearningworkshopnips2010.wordpress.com/schedule/oral1 .

Socher, R., Lin, C., Ng, A. Y., & Manning, C. D. (2011). Parsing natural scenes and natural language with recursive neural networks. In Proc. 28th international conference on machine learning (ICML).

Sperduti, A. (1994). In advances in neural information processing systems: Vol. 5. Encoding labeled graphs by labeling RAAM. San Mateo: Morgan Kaufmann.

Ponce, J., Lazebnik, S., & Schmid, C. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In Proc. computer vision and pattern recognition (Vol. II, pp. 2169–2178).

Vapnik, V. N. (1995). The nature of statistical learning theory. Berlin: Springer.

von Ahn, L. (2006). Games with a purpose. IEEE Computer, 39(6), 92–94

Welling, M. (2009). Herding dynamic weights to learn. In Proc. 26th international conference on machine learning (pp. 1121–1128).

Weston, J., Ratle, F., & Collobert, R. (2008). Deep learning via semi-supervised embedding. In Proc. 25th international conference on machine learning (pp. 1168–1175).