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A prototypical approach to machine learning
This paper presents an overview of a research programme on machine learning which is based on the fundamental process of categorization. This work draws upon the psychological theory of prototypical concepts . This theory is that concepts learnt naturally from interaction with the environment (basic categories) are not structured or defined in logical terms but are clustered in accordance with their similaritry to a central prototype, representing the "most typical" member.
A structure of a computer model designed to achieve categorization is outlined and the knowledge representational forms and developmental learning associated with this approach are discussed
Ab initio vibrational free energies including anharmonicity for multicomponent alloys
A density-functional-theory based approach to efficiently compute numerically
exact vibrational free energies - including anharmonicity - for chemically
complex multicomponent alloys is developed. It is based on a combination of
thermodynamic integration and a machine-learning potential. We demonstrate the
performance of the approach by computing the anharmonic free energy of the
prototypical five-component VNbMoTaW refractory high entropy alloy
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learning a
distance measure which brings data points from the same class closer together
and pushes data points from different classes further apart. Recent research
has demonstrated that metric learning approaches can also be applied to trees,
such as molecular structures, abstract syntax trees of computer programs, or
syntax trees of natural language, by learning the cost function of an edit
distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree.
However, learning such costs directly may yield an edit distance which violates
metric axioms, is challenging to interpret, and may not generalize well. In
this contribution, we propose a novel metric learning approach for trees which
we call embedding edit distance learning (BEDL) and which learns an edit
distance indirectly by embedding the tree nodes as vectors, such that the
Euclidean distance between those vectors supports class discrimination. We
learn such embeddings by reducing the distance to prototypical trees from the
same class and increasing the distance to prototypical trees from different
classes. In our experiments, we show that BEDL improves upon the
state-of-the-art in metric learning for trees on six benchmark data sets,
ranging from computer science over biomedical data to a natural-language
processing data set containing over 300,000 nodes.Comment: Paper at the International Conference of Machine Learning (2018),
2018-07-10 to 2018-07-15 in Stockholm, Swede
Pixel-Grounded Prototypical Part Networks
Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its
derivatives, are an intrinsically interpretable approach to machine learning.
Their prototype learning scheme enables intuitive explanations of the form,
this (prototype) looks like that (testing image patch). But, does this actually
look like that? In this work, we delve into why object part localization and
associated heat maps in past work are misleading. Rather than localizing to
object parts, existing ProtoPartNNs localize to the entire image, contrary to
generated explanatory visualizations. We argue that detraction from these
underlying issues is due to the alluring nature of visualizations and an
over-reliance on intuition. To alleviate these issues, we devise new receptive
field-based architectural constraints for meaningful localization and a
principled pixel space mapping for ProtoPartNNs. To improve interpretability,
we propose additional architectural improvements, including a simplified
classification head. We also make additional corrections to PROTOPNET and its
derivatives, such as the use of a validation set, rather than a test set, to
evaluate generalization during training. Our approach, PIXPNET (Pixel-grounded
Prototypical part Network), is the only ProtoPartNN that truly learns and
localizes to prototypical object parts. We demonstrate that PIXPNET achieves
quantifiably improved interpretability without sacrificing accuracy.Comment: 21 page
The Statistical Physics of Learning Revisited:Typical Learning Curves in Model Scenarios
The exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.</p
Digital image forensics via meta-learning and few-shot learning
Digital images are a substantial portion of the information conveyed by social media, the Internet, and television in our daily life. In recent years, digital images have become not only one of the public information carriers, but also a crucial piece of evidence. The widespread availability of low-cost, user-friendly, and potent image editing software and mobile phone applications facilitates altering images without professional expertise. Consequently, safeguarding the originality and integrity of digital images has become a difficulty. Forgers commonly use digital image manipulation to transmit misleading information. Digital image forensics investigates the irregular patterns that might result from image alteration. It is crucial to information security.
Over the past several years, machine learning techniques have been effectively used to identify image forgeries. Convolutional Neural Networks(CNN) are a frequent machine learning approach. A standard CNN model could distinguish between original and manipulated images. In this dissertation, two CNN models are introduced to recognize seam carving and Gaussian filtering.
Training a conventional CNN model for a new similar image forgery detection task, one must start from scratch. Additionally, many types of tampered image data are challenging to acquire or simulate.
Meta-learning is an alternative learning paradigm in which a machine learning model gets experience across numerous related tasks and uses this expertise to improve its future learning performance. Few-shot learning is a method for acquiring knowledge from few data. It can classify images with as few as one or two examples per class. Inspired by meta-learning and few-shot learning, this dissertation proposed a prototypical networks model capable of resolving a collection of related image forgery detection problems. Unlike traditional CNN models, the proposed prototypical networks model does not need to be trained from scratch for a new task. Additionally, it drastically decreases the quantity of training images
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