5,447 research outputs found
A Coq-based synthesis of Scala programs which are correct-by-construction
The present paper introduces Scala-of-Coq, a new compiler that allows a
Coq-based synthesis of Scala programs which are "correct-by-construction". A
typical workflow features a user implementing a Coq functional program, proving
this program's correctness with regards to its specification and making use of
Scala-of-Coq to synthesize a Scala program that can seamlessly be integrated
into an existing industrial Scala or Java application.Comment: 2 pages, accepted version of the paper as submitted to FTfJP 2017
(Formal Techniques for Java-like Programs), June 18-23, 2017, Barcelona ,
Spai
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
To understand diverse natural language commands, virtual assistants today are
trained with numerous labor-intensive, manually annotated sentences. This paper
presents a methodology and the Genie toolkit that can handle new compound
commands with significantly less manual effort. We advocate formalizing the
capability of virtual assistants with a Virtual Assistant Programming Language
(VAPL) and using a neural semantic parser to translate natural language into
VAPL code. Genie needs only a small realistic set of input sentences for
validating the neural model. Developers write templates to synthesize data;
Genie uses crowdsourced paraphrases and data augmentation, along with the
synthesized data, to train a semantic parser. We also propose design principles
that make VAPL languages amenable to natural language translation. We apply
these principles to revise ThingTalk, the language used by the Almond virtual
assistant. We use Genie to build the first semantic parser that can support
compound virtual assistants commands with unquoted free-form parameters. Genie
achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's
generality by showing a 19% and 31% improvement over the previous state of the
art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201
Recommended from our members
A multimodal restaurant finder for semantic web
Multimodal dialogue systems provide multiple modalities in the form of speech, mouse clicking, drawing or touch that can enhance human-computer interaction. However, one of the drawbacks of the existing multimodal systems is that they are highly domain-specific and they do not allow information to be shared across different providers. In this paper, we propose a semantic multimodal system, called Semantic Restaurant Finder, for the Semantic Web in which the restaurant information in different city/country/language are constructed as ontologies to allow the information to be sharable. From the Semantic Restaurant Finder, users can make use of the semantic restaurant knowledge distributed from different locations on the Internet to find the desired restaurants
CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.Comment: Accepted at CVPR-201
Phoneme Recognition Using Acoustic Events
This paper presents a new approach to phoneme recognition using nonsequential
sub--phoneme units. These units are called acoustic events and are
phonologically meaningful as well as recognizable from speech signals. Acoustic
events form a phonologically incomplete representation as compared to
distinctive features. This problem may partly be overcome by incorporating
phonological constraints. Currently, 24 binary events describing manner and
place of articulation, vowel quality and voicing are used to recognize all
German phonemes. Phoneme recognition in this paradigm consists of two steps:
After the acoustic events have been determined from the speech signal, a
phonological parser is used to generate syllable and phoneme hypotheses from
the event lattice. Results obtained on a speaker--dependent corpus are
presented.Comment: 4 pages, to appear at ICSLP'94, PostScript version (compressed and
uuencoded
- …