@article {3764, title = {Recurrent computations for visual pattern completion}, journal = {Proceedings of the National Academy of Sciences}, year = {2018}, month = {08/2018}, abstract = {

Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered \<15\% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.

}, keywords = {Artificial Intelligence, computational neuroscience, Machine Learning, pattern completion, Visual object recognition}, issn = {0027-8424}, doi = {10.1073/pnas.1719397115}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1719397115}, author = {Hanlin Tang and Martin Schrimpf and William Lotter and Moerman, Charlotte and Paredes, Ana and Ortega Caro, Josue and Hardesty, Walter and David Cox and Gabriel Kreiman} } @article {2557, title = {Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review}, journal = {International Journal of Automation and Computing}, year = {2017}, month = {03/2017}, pages = {1-17}, abstract = {

The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.

}, keywords = {convolutional neural networks, deep and shallow networks, deep learning, function approximation, Machine Learning, Neural Networks}, doi = {10.1007/s11633-017-1054-2}, url = {http://link.springer.com/article/10.1007/s11633-017-1054-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst}, author = {Tomaso Poggio and Hrushikesh Mhaskar and Lorenzo Rosasco and Brando Miranda and Qianli Liao} } @article {2326, title = {Measuring and modeling the perception of natural and unconstrained gaze in humans and machines}, year = {2016}, month = {11/2016}, abstract = {

Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes? These questions pose a challenge from both empirical and computational perspectives, due to the complexity of the visual input in real-life situations. Here we measure empirically human accuracy in perceiving the gaze direction of others in lifelike scenes, and study computationally the sources of information and representations underlying this cognitive capacity. We show that humans perform better in face-to-face conditions compared with recorded conditions, and that this advantage is not due to the availability of input dynamics. We further show that humans are still performing well when only the eyes-region is visible, rather than the whole face. We develop a computational model, which replicates the pattern of human performance, including the finding that the eyes-region contains on its own, the required information for estimating both head orientation and direction of gaze. Consistent with neurophysiological findings on task-specific face regions in the brain, the learned computational representations reproduce perceptual effects such as the Wollaston illusion, when trained to estimate direction of gaze, but not when trained to recognize objects or faces.

}, keywords = {computational evaluation, computational modeling, Computer vision, empirical evaluation, estimation of gaze direction, Gaze perception, joint attention, Machine Learning}, author = {Daniel Harari and Tao Gao and Nancy Kanwisher and Joshua B. Tenenbaum and Shimon Ullman} } @article {1508, title = {Holographic Embeddings of Knowledge Graphs}, year = {2015}, month = {11/16/2015}, abstract = {

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.

}, keywords = {Associative Memory, Knowledge Graph, Machine Learning}, author = {Maximilian Nickel and Lorenzo Rosasco and Tomaso Poggio} } @article {1566, title = {Human-level concept learning through probabilistic program induction}, journal = {Science}, volume = {350}, year = {2015}, month = {12/11/2015}, pages = {1332-1338 }, abstract = {

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms{\textemdash}for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world{\textquoteright}s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several {\textquotedblleft}visual Turing tests{\textquotedblright} probing the model{\textquoteright}s creative generalization abilities, which in many cases are indistinguishable from human behavior.

}, keywords = {Machine Learning}, doi = {10.1126/science.aab3050 }, url = {http://www.sciencemag.org/content/350/6266/1332.short}, author = {Brenden M Lake and Salakhutdinov, Ruslan and Joshua B. Tenenbaum} } @article {227, title = {A Deep Representation for Invariance And Music Classification}, number = {002}, year = {2014}, month = {03/2014}, abstract = {

Representations in the auditory cortex might be based on mechanisms similar to the visual ventral stream; modules for building invariance to transformations and multiple layers for compositionality and selectivity. In this paper we propose the use of such computational modules for extracting invariant and discriminative audio representations. Building on a theory of invariance in hierarchical architectures, we propose a novel, mid-level representation for acoustical signals, using the empirical distributions of projections on a set of templates and their transformations. Under the assumption that, by construction, this dictionary of templates is composed from similar classes, and samples the orbit of variance-inducing signal transformations (such as shift and scale), the resulting signature is theoretically guaranteed to be unique, invariant to transformations and stable to deformations. Modules of projection and pooling can then constitute layers of deep networks, for learning composite representations. We present the main theoretical and computational aspects of a framework for unsupervised learning of invariant audio representations, empirically evaluated on music genre classification.

}, keywords = {Audio Representation, Hierarchy, Invariance, Machine Learning, Theories for Intelligence}, author = {Chiyuan Zhang and Georgios Evangelopoulos and Stephen Voinea and Lorenzo Rosasco and Tomaso Poggio} }