76,615 research outputs found
Learning by stochastic serializations
Complex structures are typical in machine learning. Tailoring learning
algorithms for every structure requires an effort that may be saved by defining
a generic learning procedure adaptive to any complex structure. In this paper,
we propose to map any complex structure onto a generic form, called
serialization, over which we can apply any sequence-based density estimator. We
then show how to transfer the learned density back onto the space of original
structures. To expose the learning procedure to the structural particularities
of the original structures, we take care that the serializations reflect
accurately the structures' properties. Enumerating all serializations is
infeasible. We propose an effective way to sample representative serializations
from the complete set of serializations which preserves the statistics of the
complete set. Our method is competitive or better than state of the art
learning algorithms that have been specifically designed for given structures.
In addition, since the serialization involves sampling from a combinatorial
process it provides considerable protection from overfitting, which we clearly
demonstrate on a number of experiments.Comment: Submission to NeurIPS 201
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Context-Aware Zero-Shot Recognition
We present a novel problem setting in zero-shot learning, zero-shot object
recognition and detection in the context. Contrary to the traditional zero-shot
learning methods, which simply infers unseen categories by transferring
knowledge from the objects belonging to semantically similar seen categories,
we aim to understand the identity of the novel objects in an image surrounded
by the known objects using the inter-object relation prior. Specifically, we
leverage the visual context and the geometric relationships between all pairs
of objects in a single image, and capture the information useful to infer
unseen categories. We integrate our context-aware zero-shot learning framework
into the traditional zero-shot learning techniques seamlessly using a
Conditional Random Field (CRF). The proposed algorithm is evaluated on both
zero-shot region classification and zero-shot detection tasks. The results on
Visual Genome (VG) dataset show that our model significantly boosts performance
with the additional visual context compared to traditional methods
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