110 research outputs found
Regularization-Based Methods for Ordinal Quantification
Quantification, i.e., the task of training predictors of the class prevalence
values in sets of unlabeled data items, has received increased attention in
recent years. However, most quantification research has concentrated on
developing algorithms for binary and multiclass problems in which the classes
are not ordered. Here, we study the ordinal case, i.e., the case in which a
total order is defined on the set of n>2 classes. We give three main
contributions to this field. First, we create and make available two datasets
for ordinal quantification (OQ) research that overcome the inadequacies of the
previously available ones. Second, we experimentally compare the most important
OQ algorithms proposed in the literature so far. To this end, we bring together
algorithms proposed by authors from very different research fields, such as
data mining and astrophysics, who were unaware of each others' developments.
Third, we propose a novel class of regularized OQ algorithms, which outperforms
existing algorithms in our experiments. The key to this gain in performance is
that our regularization prevents ordinally implausible estimates, assuming that
ordinal distributions tend to be smooth in practice. We informally verify this
assumption for several real-world applications.Comment: 45 page
A Framework for End-to-End Learning on Semantic Tree-Structured Data
While learning models are typically studied for inputs in the form of a fixed
dimensional feature vector, real world data is rarely found in this form. In
order to meet the basic requirement of traditional learning models, structural
data generally have to be converted into fix-length vectors in a handcrafted
manner, which is tedious and may even incur information loss. A common form of
structured data is what we term "semantic tree-structures", corresponding to
data where rich semantic information is encoded in a compositional manner, such
as those expressed in JavaScript Object Notation (JSON) and eXtensible Markup
Language (XML). For tree-structured data, several learning models have been
studied to allow for working directly on raw tree-structure data, However such
learning models are limited to either a specific tree-topology or a specific
tree-structured data format, e.g., synthetic parse trees. In this paper, we
propose a novel framework for end-to-end learning on generic semantic
tree-structured data of arbitrary topology and heterogeneous data types, such
as data expressed in JSON, XML and so on. Motivated by the works in recursive
and recurrent neural networks, we develop exemplar neural implementations of
our framework for the JSON format. We evaluate our approach on several UCI
benchmark datasets, including ablation and data-efficiency studies, and on a
toy reinforcement learning task. Experimental results suggest that our
framework yields comparable performance to use of standard models with
dedicated feature-vectors in general, and even exceeds baseline performance in
cases where compositional nature of the data is particularly important.
The source code for a JSON-based implementation of our framework along with
experiments can be downloaded at https://github.com/EndingCredits/json2vec.Comment: This is a preliminary version of our work. The project is still
ongoing. The source code for a JSON-based implementation of our framework
along with experiments can be downloaded at
https://github.com/EndingCredits/json2ve
From Form to Function: Detecting the Affordance of Tool Parts using Geometric Features and Material Cues
With recent advances in robotics, general purpose robots like Baxter are
quickly becoming a reality. As robots begin to collaborate with humans in everyday
workspaces, they will need to understand the functions of objects and their
parts. To cut an apple or hammer a nail, robots need to not just know a tool’s name,
but they must find its parts and identify their potential functions, or affordances.
As Gibson remarked, “If you know what can be done with a[n] object, what it can
be used for, you can call it whatever you please.”
We hypothesize that the geometry of a part is closely related to its affordance,
since its geometric properties govern the possible physical interactions with the environment.
In the first part of this thesis, we investigate how the affordances of tool
parts can be predicted using geometric features from RGB-D sensors like Kinect.
We develop several approaches to learn affordance from geometric features: using
superpixel based hierarchical sparse coding, structured random forests, and convolutional
neural networks. To evaluate the proposed methods, we construct a large
RGB-D dataset where parts are labeled with multiple affordances. Experiments
over sequences containing clutter, occlusions, and viewpoint changes show that the
approaches provide precise predictions that can be used in robotics applications.
In addition to geometry, the material properties of a part also determine its
potential functions. In the second part of this thesis, we investigate how material
cues can be integrated into a deep learning framework for affordance prediction. We
propose a modular approach for combining high-level material information, or other
mid-level cues, in order to improve affordance predictions. We present experiments
which demonstrate the efficacy of our approach on an expanded RGB-D dataset,
which includes data from non-tool objects and multiple depth sensors. The work
presented in this thesis lays a foundation for the development of robots which can
predict the potential functions of tool parts, and provides a basis for higher level
reasoning about affordance
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
Symbolic and Deep Learning Based Data Representation Methods for Activity Recognition and Image Understanding at Pixel Level
Efficient representation of large amount of data particularly images and video helps in the analysis, processing and overall understanding of the data. In this work, we present two frameworks that encapsulate the information present in such data. At first, we present an automated symbolic framework to recognize particular activities in real time from videos. The framework uses regular expressions for symbolically representing (possibly infinite) sets of motion characteristics obtained from a video. It is a uniform framework that handles trajectory-based and periodic articulated activities and provides polynomial time graph algorithms for fast recognition. The regular expressions representing motion characteristics can either be provided manually or learnt automatically from positive and negative examples of strings (that describe dynamic behavior) using offline automata learning frameworks. Confidence measures are associated with recognitions using Levenshtein distance between a string representing a motion signature and the regular expression describing an activity. We have used our framework to recognize trajectory-based activities like vehicle turns (U-turns, left and right turns, and K-turns), vehicle start and stop, person running and walking, and periodic articulated activities like digging, waving, boxing, and clapping in videos from the VIRAT public dataset, the KTH dataset, and a set of videos obtained from YouTube. Next, we present a core sampling framework that is able to use activation maps from several layers of a Convolutional Neural Network (CNN) as features to another neural network using transfer learning to provide an understanding of an input image. The intermediate map responses of a Convolutional Neural Network (CNN) contain information about an image that can be used to extract contextual knowledge about it. Our framework creates a representation that combines features from the test data and the contextual knowledge gained from the responses of a pretrained network, processes it and feeds it to a separate Deep Belief Network. We use this representation to extract more information from an image at the pixel level, hence gaining understanding of the whole image. We experimentally demonstrate the usefulness of our framework using a pretrained VGG-16 model to perform segmentation on the BAERI dataset of Synthetic Aperture Radar (SAR) imagery and the CAMVID dataset. Using this framework, we also reconstruct images by removing noise from noisy character images. The reconstructed images are encoded using Quadtrees. Quadtrees can be an efficient representation in learning from sparse features. When we are dealing with handwritten character images, they are quite susceptible to noise. Hence, preprocessing stages to make the raw data cleaner can improve the efficacy of their use. We improve upon the efficiency of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from the images. The pixel level denoiser uses a pretrained CNN trained on a large image dataset and uses transfer learning to aid the reconstruction of characters. In this work, we primarily deal with classification of noisy characters and create the noisy versions of handwritten Bangla Numeral and Basic Character datasets and use them and the Noisy MNIST dataset to demonstrate the usefulness of our approach
Multivariate Statistical Machine Learning Methods for Genomic Prediction
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool
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