247,024 research outputs found
IST Austria Thesis
Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction
for tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually
dissimilar domains
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Validating specifications of dynamic systems using automated reasoning techniques
In this paper, we propose a new approach to validating formal specifications of observable behavior of discrete dynamic systems. By observable behavior we mean system behavior as observed by users or other systems in the environment of the system. Validation of a formal specification of an informal domain tries to answer the question whether the specification actually describes the intended domain. This differs from the verification problem, which deals with the correspondence between formal objects, e.g. between a formal specification of a system and an implementation of it. We consider formal specifications of object-oriented dynamic systems that are subject to static and dynamic integrity constraints. To validate that such a specification expresses the intended behavior, we propose to use a tool that can answer reachability queries. In a reachability query we ask whether the system can evolve from one state into another without violating the integrity constraints. If the query is answered positively, the system should exhibit an example path between the states; if the answer is negative, the system should explain why this is so. An example path produced by the tool can be used to produce scenarios for presentations of system behavior, but can also be used as a basis for acceptance testing. In this paper, we discuss the use of planning and theoremproving techniques to answer such queries, and illustrate the use of reachability queries in the context of information system development
Unified Pragmatic Models for Generating and Following Instructions
We show that explicit pragmatic inference aids in correctly generating and
following natural language instructions for complex, sequential tasks. Our
pragmatics-enabled models reason about why speakers produce certain
instructions, and about how listeners will react upon hearing them. Like
previous pragmatic models, we use learned base listener and speaker models to
build a pragmatic speaker that uses the base listener to simulate the
interpretation of candidate descriptions, and a pragmatic listener that reasons
counterfactually about alternative descriptions. We extend these models to
tasks with sequential structure. Evaluation of language generation and
interpretation shows that pragmatic inference improves state-of-the-art
listener models (at correctly interpreting human instructions) and speaker
models (at producing instructions correctly interpreted by humans) in diverse
settings.Comment: NAACL 2018, camera-ready versio
Combining Expression and Content in Domains for Dialog Managers
We present work in progress on abstracting dialog managers from their domain
in order to implement a dialog manager development tool which takes (among
other data) a domain description as input and delivers a new dialog manager for
the described domain as output. Thereby we will focus on two topics; firstly,
the construction of domain descriptions with description logics and secondly,
the interpretation of utterances in a given domain.Comment: 5 pages, uses conference.st
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Understanding analogical reasoning : viewpoints from psychology and related disciplines
Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning
Early aspects: aspect-oriented requirements engineering and architecture design
This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications
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