2,406 research outputs found
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Practicing at Home: Computers, Pianos, and Cultural Capital
Part of the Volume on Digital Young, Innovation, and the Unexpected Bourdieu focused attention on the role of education and the influence of status distinctions on the selection and valorization of particular forms of cultural capital. Although Bourdieu did not write about digital media, he was a keen observer of status distinctions in education and how these translate into job markets. Through an extended analogy between learning the piano and learning the computer, I demonstrate Bourdieu's relevance for an expanded vision of digital literacy -- one that would forefront the material and social inequalities in U.S. domestic Internet access and in public education. High Tech High School, supported by the Gates Foundation, provides a case of why it is important to examine current digital pedagogy in terms of unarticulated and implicit models of entrepreneurial labor, both because these set up unrealistic expectations and because they can express corporate norms rather than critical pedagogy
A Platform-independent Programming Environment for Robot Control
The development of robot control programs is a complex task. Many robots are
different in their electrical and mechanical structure which is also reflected
in the software. Specific robot software environments support the program
development, but are mainly text-based and usually applied by experts in the
field with profound knowledge of the target robot. This paper presents a
graphical programming environment which aims to ease the development of robot
control programs. In contrast to existing graphical robot programming
environments, our approach focuses on the composition of parallel action
sequences. The developed environment allows to schedule independent robot
actions on parallel execution lines and provides mechanism to avoid
side-effects of parallel actions. The developed environment is
platform-independent and based on the model-driven paradigm. The feasibility of
our approach is shown by the application of the sequencer to a simulated
service robot and a robot for educational purpose
Towards a property graph generator for benchmarking
The use of synthetic graph generators is a common practice among
graph-oriented benchmark designers, as it allows obtaining graphs with the
required scale and characteristics. However, finding a graph generator that
accurately fits the needs of a given benchmark is very difficult, thus
practitioners end up creating ad-hoc ones. Such a task is usually
time-consuming, and often leads to reinventing the wheel. In this paper, we
introduce the conceptual design of DataSynth, a framework for property graphs
generation with customizable schemas and characteristics. The goal of DataSynth
is to assist benchmark designers in generating graphs efficiently and at scale,
saving from implementing their own generators. Additionally, DataSynth
introduces novel features barely explored so far, such as modeling the
correlation between properties and the structure of the graph. This is achieved
by a novel property-to-node matching algorithm for which we present preliminary
promising results
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