8,438 research outputs found
SEPIA: Search for Proofs Using Inferred Automata
This paper describes SEPIA, a tool for automated proof generation in Coq.
SEPIA combines model inference with interactive theorem proving. Existing proof
corpora are modelled using state-based models inferred from tactic sequences.
These can then be traversed automatically to identify proofs. The SEPIA system
is described and its performance evaluated on three Coq datasets. Our results
show that SEPIA provides a useful complement to existing automated tactics in
Coq.Comment: To appear at 25th International Conference on Automated Deductio
Mining State-Based Models from Proof Corpora
Interactive theorem provers have been used extensively to reason about
various software/hardware systems and mathematical theorems. The key challenge
when using an interactive prover is finding a suitable sequence of proof steps
that will lead to a successful proof requires a significant amount of human
intervention. This paper presents an automated technique that takes as input
examples of successful proofs and infers an Extended Finite State Machine as
output. This can in turn be used to generate proofs of new conjectures. Our
preliminary experiments show that the inferred models are generally accurate
(contain few false-positive sequences) and that representing existing proofs in
such a way can be very useful when guiding new ones.Comment: To Appear at Conferences on Intelligent Computer Mathematics 201
A System of Interaction and Structure II: The Need for Deep Inference
This paper studies properties of the logic BV, which is an extension of
multiplicative linear logic (MLL) with a self-dual non-commutative operator. BV
is presented in the calculus of structures, a proof theoretic formalism that
supports deep inference, in which inference rules can be applied anywhere
inside logical expressions. The use of deep inference results in a simple
logical system for MLL extended with the self-dual non-commutative operator,
which has been to date not known to be expressible in sequent calculus. In this
paper, deep inference is shown to be crucial for the logic BV, that is, any
restriction on the ``depth'' of the inference rules of BV would result in a
strictly less expressive logical system
ParseNet: Looking Wider to See Better
We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baseline networks (e.g. from FCN). When we add our proposed
global feature, and a technique for learning normalization parameters, accuracy
increases consistently even over our improved versions of the baselines. Our
proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow
and PASCAL-Context with small additional computational cost over baselines, and
near current state-of-the-art performance on PASCAL VOC 2012 semantic
segmentation with a simple approach. Code is available at
https://github.com/weiliu89/caffe/tree/fcn .Comment: ICLR 2016 submissio
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
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