GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.Comment: Corresponding author: Kai Xu ([email protected]

Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

The knowledge that shapes the city:the human city beneath the social city

In the Atlanta Symposium (Hillier, 2001, 2003a) a theory of the social constructionof the city was presented. In this paper it is proposed that underlying the variouskinds of social city there is a deeper, more generic human city, which arises from thepervasive intervention of the human cognitive subject in the shaping and workingof the city. This intervention is explored at two critical stages in the forming of thecity: in the 'vertical' form-creating process by which the accumulation of built formscreates an emergent spatial pattern; and in the 'lateral' form-function process bywhich the emergent spatial pattern shapes movement and sets off the process bywhich an aggregate of buildings becomes a living city. The nature of these cognitiveinterventions is investigated by asking a question: how do human beings 'synchronise'diachronically acquired (and diachronically created) spatial information into asynchronic picture of ambient urban spatial patterns, since it is such synchronicpictures which seem to mediate both interventions? A possible answer is sought bydeveloping the concept of 'description retrieval', originally proposed in 'The SocialLogic of Space' as the means by which human beings retrieve abstract informationfrom patterns of relations in the real world. Our ability to retrieve such descriptionhappens, it is argued, at more than one level, and can includes the high-level notionsof the grid which seems to plays a key role in cognitive intervention in the city.Finally we ask what the ubiquity of the human cognitive subject in the formation ofthe city implies for how we should see cities as complex systems. It is argued that,as with language, there is a 'objective subject' at the heart of the processes by whichcities come into existence, and that this provides us both with the need and themeans to mediate between the social physics paradigm of the city, with its focus onthe mathematics of the generation of the physical city and phenomenologicalparadigm with its - too often anti-mathematical - focus on the human experience ofthe city. Since the intervention of the cognitive subject involves formal ideas andhas formal consequences for the structure of the city, we cannot, it is argued, explaineither without the other

Updating Ambiguity Averse Preferences

Dynamic consistency leads to Bayesian updating under expected utility. We ask what it implies for the updating of more general preferences. In this paper, we charac- terize dynamically consistent update rules for preference models satisfying ambiguity aversion. This characterization extends to regret-based models as well. As an appli- cation of our general result, we characterize dynamically consistent updating for two important models of ambiguity averse preferences: the ambiguity averse smooth am- biguity preferences (Klibanoff, Marinacci and Mukerji [Econometrica 73 2005, pp. 1849-1892]) and the variational preferences (Maccheroni, Marinacci and Rustichini [Econometrica 74 2006, pp. 1447-1498]). The latter includes max-min expected utility (Gilboa and Schmeidler [Journal of Mathematical Economics 18 1989, pp. 141-153]) and the multiplier preferences of Hansen and Sargent [American Economic Review 91(2) 2001, pp. 60-66] as special cases. For smooth ambiguity preferences, we also identify a simple rule that is shown to be the unique dynamically consistent rule among a large class of rules that may be expressed as reweightings of Bayes's rule.Updating, Dynamic Consistency, Ambiguity, Regret, Ellsberg, Bayesian, Consequentialism, Smooth Ambiguity

Markov Equilibrium in Models of Dynamic Endogenous Political Institutions

This paper examines existence of Markov equilibria in the class of dynamic political games (DPGs). DPGs are dynamic games in which political institutions are endogenously determined each period. The process of change is both recursive and instrumental: the rules for political aggregation at date t+1 are decided by the rules at date t, and the resulting institutional choices do not affect payoffs or technology directly. Equilibrium existence in dynamic political games requires a resolution to a “political fixed point problem” in which a current political rule (e.g., majority voting) admits a solution only if all feasible political rules in the future admit solutions in all states. If the class of political rules is dynamically consistent, then DPGs are shown to admit political fixed points. This result is used to prove two equilibrium existence theorems, one of which implies that equilibrium strategies, public and private, are smooth functions of the economic state. We discuss practical applications that require existence of smooth equilibria.Recursive, dynamic political games, political fixed points, dynamically consistent rules.