22,444 research outputs found
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
Evolutionary multitasking has recently emerged as a novel paradigm that
enables the similarities and/or latent complementarities (if present) between
distinct optimization tasks to be exploited in an autonomous manner simply by
solving them together with a unified solution representation scheme. An
important matter underpinning future algorithmic advancements is to develop a
better understanding of the driving force behind successful multitask
problem-solving. In this regard, two (seemingly disparate) ideas have been put
forward, namely, (a) implicit genetic transfer as the key ingredient
facilitating the exchange of high-quality genetic material across tasks, and
(b) population diversification resulting in effective global search of the
unified search space encompassing all tasks. In this paper, we present some
empirical results that provide a clearer picture of the relationship between
the two aforementioned propositions. For the numerical experiments we make use
of Sudoku puzzles as case studies, mainly because of their feature that
outwardly unlike puzzle statements can often have nearly identical final
solutions. The experiments reveal that while on many occasions genetic transfer
and population diversity may be viewed as two sides of the same coin, the wider
implication of genetic transfer, as shall be shown herein, captures the true
essence of evolutionary multitasking to the fullest.Comment: 7 pages, 6 figure
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of Boltzmann and Shannon but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the first derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007
joint invited lectur
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
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