@article {443, title = {Computational role of eccentricity dependent cortical magnification.}, number = {017}, year = {2014}, month = {06/2014}, abstract = {

We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea {\textemdash} in agreement with data about the cortical magnification factor, V1 and the retina. From the slope of the inverse of the magnification factor, M-theory predicts a cortical {\textquotedblleft}fovea{\textquotedblright} in V1 in the order of 40 by 40 basic units at each receptive field size {\textemdash} corresponding to a foveola of size around 26 minutes of arc at the highest resolution, ≈6 degrees at the lowest resolution. It also predicts uniform scale invariance over a fixed range of scales independently of eccentricity, while translation invariance should depend linearly on spatial frequency. Bouma{\textquoteright}s law of crowding follows in the theory as an effect of cortical area-by-cortical area pooling; the Bouma constant is the value expected if the signature responsible for recognition in the crowding experiments originates in V2. From a broader perspective, the emerging picture suggests that visual recognition under natural conditions takes place by composing information from a set of fixations, with each fixation providing recognition from a space-scale image fragment {\textemdash} that is an image patch represented at a set of increasing sizes and decreasing resolutions.

}, keywords = {Invariance, Theories for Intelligence}, author = {Tomaso Poggio and Jim Mutch and Leyla Isik} } @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} } @article {438, title = {The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex}, number = {004}, year = {2014}, month = {04/2014}, abstract = {

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system{\textquoteright}s optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

}, keywords = {Neuroscience, Theories for Intelligence}, doi = {10.1101/004473}, url = {http://biorxiv.org/lookup/doi/10.1101/004473}, author = {JZ. Leibo and Qianli Liao and F. Anselmi and Tomaso Poggio} } @article {451, title = {Learning An Invariant Speech Representation}, number = {022}, year = {2014}, month = {06/2014}, abstract = {

Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input. We formulate the problem of finding robust speech features for supervised learning with small sample complexity as a problem of learning representations of the signal that are maximally invariant to intraclass transformations and deformations. We propose an extension of a theory for unsupervised learning of invariant visual representations to the auditory domain and empirically evaluate its validity for voiced speech sound classification. Our version of the theory requires the memory-based, unsupervised storage of acoustic templates {\textemdash} such as specific phones or words {\textemdash} together with all the transformations of each that normally occur. A quasi-invariant representation for a speech segment can be obtained by projecting it to each template orbit, i.e., the set of transformed signals, and computing the associated one-dimensional empirical probability distributions. The computations can be performed by modules of filtering and pooling, and extended to hierarchical architectures. In this paper, we apply a single-layer, multicomponent representation for phonemes and demonstrate improved accuracy and decreased sample complexity for vowel classification compared to standard spectral, cepstral and perceptual features.

}, keywords = {Theories for Intelligence}, author = {Georgios Evangelopoulos and Stephen Voinea and Chiyuan Zhang and Lorenzo Rosasco and Tomaso Poggio} } @article {225, title = {Neural Mechanisms of Object-Based Attention}, journal = {Science}, volume = {344}, year = {2014}, month = {04/2014}, pages = {424 - 427}, abstract = {

How we attend to objects and their features that cannot be separated by location is not understood. We presented two temporally and spatially overlapping streams of objects, faces versus houses, and used magnetoencephalography and functional magnetic resonance imaging to separate neuronal responses to attended and unattended objects. Attention to faces versus houses enhanced the sensory responses in the fusiform face area (FFA) and parahippocampal place area (PPA), respectively. The increases in sensory responses were accompanied by induced gamma synchrony between the inferior frontal junction, IFJ, and either FFA or PPA, depending on which object was attended. The IFJ appeared to be the driver of the synchrony, as gamma phases were advanced by 20 ms in IFJ compared to FFA or PPA. Thus, the IFJ may direct the flow of visual processing during object-based attention, at least in part through coupled oscillations with specialized areas such as FFA and PPA.

}, keywords = {Face recognition, Theories for Intelligence, Visual Intelligence}, issn = {0036-8075}, doi = {10.1126/science.1247003}, url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1247003}, author = {Baldauf, D. and R. Desimone} } @article {450, title = {Neural tuning size is a key factor underlying holistic face processing.}, number = {021}, year = {2014}, month = {06/2014}, abstract = {

Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person{\textquoteright}s life, and face recognition is crucial for daily social interaction. Faces are also unlike any other stimulus class in terms of certain physical stimulus characteristics. Furthermore, faces have been empirically found to elicit certain characteristic behavioral phenomena, which are widely held to be evidence of {\textquotedblleft}holistic{\textquotedblright} processing of faces. However, little is known about the neural mechanisms underlying such holistic face processing. In other words, for the processing of faces by the primate visual system, the input and output characteristics are relatively well known, but the internal neural computations are not. The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects. In this computational modeling work, we show that a single factor {\textendash} {\textquotedblleft}neural tuning size{\textquotedblright} {\textendash} is able to account for three key phenomena that are characteristic of face processing, namely the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole - Part Effect (WPE). Our computational proof - of - principle provides specific neural tuning properties that correspond to the poorly - understood notion of holistic face processing, and connects these neural properties to psychophysical behavior. Overall, our work provides a unified and parsimonious theoretical account for the disparate empirical data on face - specific processing, deepening the fundamental understanding of face processing

}, keywords = {Theories for Intelligence}, author = {Cheston Tan and Tomaso Poggio} } @conference {221, title = {Word-level Invariant Representations From Acoustic Waveforms}, booktitle = {INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association}, year = {2014}, publisher = {International Speech Communication Association (ISCA)}, organization = {International Speech Communication Association (ISCA)}, address = {Singapore}, abstract = {

Extracting discriminant, transformation-invariant features from raw audio signals remains a serious challenge for speech recognition. The issue of speaker variability is central to this problem, as changes in accent, dialect, gender, and age alter the sound waveform of speech units at multiple scales (phonemes, words, or phrases). Approaches for dealing with this variability have typically focused on analyzing the spectral properties of speech at the level of frames, on par with frame-level acoustic modeling usually applied to speech recognition systems. In this paper, we propose a framework for representing speech at the whole-word level and extracting features from the acoustic, temporal domain, without the need for spectral encoding or pre-processing. Leveraging recent work on unsupervised learning of invariant sensory representations, we extract a signature for a word by first projecting its raw waveform onto a set of templates and their transformations, and then forming empirical estimates of the resulting one-dimensional distributions via histograms. The representation and relevant parameters are evaluated for word classification on a series of datasets with increasing speaker-mismatch difficulty, and the results are compared to those of an MFCC-based representation.

}, keywords = {Invariance, Speech Representation, Theories for Intelligence}, url = {http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html}, author = {Stephen Voinea and Chiyuan Zhang and Georgios Evangelopoulos and Lorenzo Rosasco and Tomaso Poggio} }