@article {4570, title = {Hierarchically Local Tasks and Deep Convolutional Networks}, year = {2020}, month = {06/2020}, abstract = {

The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. Is there something special about deep convolutional networks that other learning machines do not possess? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure. These mathematical results, however, do not say which tasks are expected to have input-output functions with hierarchical locality. Among all the possible hierarchically local tasks in vision, text and speech we explore a few of them experimentally by studying how they are affected by disrupting locality in the input images. We also discuss a taxonomy of tasks ranging from local, to hierarchically local, to global and make predictions about the type of networks required to perform\  efficiently on these different types of tasks.

}, keywords = {Compositionality, Inductive Bias, perception, Theory of Deep Learning}, author = {Arturo Deza and Qianli Liao and Andrzej Banburski and Tomaso Poggio} } @proceedings {4261, title = {Draping an Elephant: Uncovering Children{\textquoteright}s Reasoning About Cloth-Covered Objects}, year = {2019}, month = {07/2019}, address = {Montreal, Canada}, abstract = {

Humans have an intuitive understanding of physics. They can predict how a physical scene will unfold, and reason about how it came to be. Adults may rely on such a physical representation for visual reasoning and recognition, going beyond visual features and capturing objects in terms of their physical properties. Recently, the use of draped objects in recognition was used to examine adult object representations in the absence of many common visual features. In this paper we examine young children{\textquoteright}s reasoning about draped objects in order to examine the develop of physical object representation. In addition, we argue that a better understanding of the development of the concept of cloth as a physical entity is worthwhile in and of itself, as it may form a basic ontological category in intuitive physical reasoning akin to liquids and solids. We use two experiments to investigate young children{\textquoteright}s (ages 3{\textendash}5) reasoning about cloth-covered objects, and find that they perform significantly above chance (though far from perfectly) indicating a representation of physical objects that can interact dynamically with the world. Children{\textquoteright}s success and failure pattern is similar across the two experiments, and we compare it to adult behavior. We find a small effect, which suggests the specific features that make reasoning about certain objects more difficult may carry into adulthood.

}, keywords = {analysis-by-synthesis, cloth, cognitive development, imagination, intuitive physics, object recognition, occlusion, perception, vision}, url = {https://mindmodeling.org/cogsci2019/papers/0506/index.html}, author = {Tomer D Ullman and Eliza Kosoy and Ilker Yildirim and Amir Arsalan Soltani and Max Siegel and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {2716, title = {Formalizing emotion concepts within a Bayesian model of theory of mind}, journal = {Current Option in Psychology}, volume = {17}, year = {2017}, month = {10/2017}, pages = {15-21}, chapter = {15}, abstract = {

Sensitivity to others{\textquoteright} emotions is foundational for many aspects of human life, yet computational models do not currently approach the sensitivity and specificity of human emotion knowledge. Perception of isolated physical expressions largely supplies ambiguous, low-dimensional, and noisy information about others{\textquoteright} emotional states. By contrast, observers attribute specific granular emotions to another person based on inferences of how she interprets (or {\textquotedblleft}appraises{\textquotedblright}) external events in relation to her other mental states (goals, beliefs, moral values, costs). These attributions share neural mechanisms with other reasoning about minds. Situating emotion concepts in a formal model of people{\textquoteright}s intuitive theories about other minds is necessary to effectively capture humans{\textquoteright} fine-grained emotion understanding.

}, keywords = {appraisal, bayes, emotion, inference, perception}, doi = {https://doi.org/10.1016/j.copsyc.2017.04.019}, url = {http://www.sciencedirect.com/science/article/pii/S2352250X17300283}, author = {Rebecca Saxe and Sean Dae Houlihan} } @article {2721, title = {Modeling emotion attributions as inference in an intuitive theory of mind.}, year = {2017}, address = {University of Wisconsin - Madison}, abstract = {

We model how people make third party emotion attributions as integration of perceptual cues and conceptual event knowledge in an intuitive causal theory of mind. Novel stimuli generated from a televised gameshow provide authentic (not staged) dynamic displays of emotion in the context of a quantifiable and repeatable game (a one-shot prisoner{\textquoteright}s dilemma). The gameshow involves public acts of cooperation, commitment, and betrayal, with stakes spanning five orders of magnitude (max ≈ $200,000), and thus supports a wide range of inferred emotions. The raw footage is separated into expression cues and contextual descriptions such that each player{\textquoteright}s emotions can be inferred from the player{\textquoteright}s reactions to the outcome (i.e. facial expressions and body postures), or from the event context (i.e. stakes, actions, and outcomes), or from both information sources together. Study participants attribute the experience of 20 nuanced emotions to the contestants based on (i) only dynamic visual emotion cues, (ii) only event descriptions, and (iii) combined dynamic visual cues and event descriptions. Principal component analysis and hierarchical clustering of emotion ratings are used to assess the dimensionality and structure of the attribution space supported by each unimodal signal as well as by the multimodal signal. The attributions are modeled using general linear regression with a priori features derived from behavioral economics and experimental psychology, including prospect theory, expected utility, and loss aversion. Finally, a Bayesian generative cue-combination model tests how effective joint conditioning on the unimodal signals is in explaining the emotion inferences participants make when given multimodal information.

}, keywords = {attribution, bayes, emotion, inference, inverse, perception}, author = {Sean Dae Houlihan and Rebecca Saxe} } @conference {4235, title = {Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks}, booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, month = {07/2017}, address = {Honolulu, HI}, abstract = {

We study the problem of learning generative models of 3D shapes. Voxels or 3D parts have been widely used as the underlying representations to build complex 3D shapes; however, voxel-based representations suffer from high memory requirements, and parts-based models require a large collection of cached or richly parametrized parts. We take an alternative approach: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce 3D shapes from these images. A multi-view representation of shapes enables generation of 3D models with fine details, as 2D depth maps and silhouettes can be modeled at a much higher resolution than 3D voxels. Moreover, our approach naturally brings the ability to recover the underlying 3D representation from depth maps of one or a few viewpoints. Experiments show that our framework can generate 3D shapes with variations and details. We also demonstrate that our model has out-of-sample generalization power for real-world tasks with occluded objects.

}, keywords = {2d to 3d, 3D generation, 3D reconstruction, Core object system, depth map, generative, perception, silhouette}, doi = {10.1109/CVPR.2017.269}, url = {http://ieeexplore.ieee.org/document/8099752/http://xplorestaging.ieee.org/ielx7/8097368/8099483/08099752.pdf?arnumber=8099752}, author = {Amir Arsalan Soltani and Haibin Huang and Jiajun Wu and Tejas Kulkarni and Joshua B. Tenenbaum} }