14,747 research outputs found
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and
probabilistic programming rely heavily on sampling from generally intractable
probability distributions. There is increasing interest in the potential
advantages of using quantum computing technologies as sampling engines to speed
up these tasks or to make them more effective. However, some pressing
challenges in state-of-the-art quantum annealers have to be overcome before we
can assess their actual performance. The sparse connectivity, resulting from
the local interaction between quantum bits in physical hardware
implementations, is considered the most severe limitation to the quality of
constructing powerful generative unsupervised machine-learning models. Here we
use embedding techniques to add redundancy to data sets, allowing us to
increase the modeling capacity of quantum annealers. We illustrate our findings
by training hardware-embedded graphical models on a binarized data set of
handwritten digits and two synthetic data sets in experiments with up to 940
quantum bits. Our model can be trained in quantum hardware without full
knowledge of the effective parameters specifying the corresponding quantum
Gibbs-like distribution; therefore, this approach avoids the need to infer the
effective temperature at each iteration, speeding up learning; it also
mitigates the effect of noise in the control parameters, making it robust to
deviations from the reference Gibbs distribution. Our approach demonstrates the
feasibility of using quantum annealers for implementing generative models, and
it provides a suitable framework for benchmarking these quantum technologies on
machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys.
Rev.
The essence of P2P: A reference architecture for overlay networks
The success of the P2P idea has created a huge diversity
of approaches, among which overlay networks, for example,
Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS,
have received specific attention from both developers and
researchers. A wide variety of algorithms, data structures,
and architectures have been proposed. The terminologies
and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable
Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules
Link prediction is an important task in addressing the incompleteness problem
of knowledge graphs (KG). Previous link prediction models suffer from issues
related to either performance or explanatory capability. Furthermore, models
that are capable of generating explanations, often struggle with erroneous
paths or reasoning leading to the correct answer. To address these challenges,
we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and
Slow Thinking with Filtered rules for Link Prediction task), inspired by two
distinct aspects of human cognition: "commonsense reasoning" and "thinking,
fast and slow." Our objective is to combine a logical and neural model for
enhanced link prediction. To tackle the challenge of dealing with incorrect
paths or rules generated by the logical model, we propose a semi-supervised
method to convert rules into sentences. These sentences are then subjected to
assessment and removal of incorrect rules using an NLI (Natural Language
Inference) model. Our approach to combining logical and neural models involves
first obtaining answers from both the logical and neural models. These answers
are subsequently unified using an Inference Engine module, which has been
realized through both algorithmic implementation and a novel neural model
architecture. To validate the efficacy of our model, we conducted a series of
experiments. The results demonstrate the superior performance of our model in
both link prediction metrics and the generation of more reliable explanations
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