1,167 research outputs found
CoupledCF: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better explain how and why a user has personalized preference on an item. This work builds on non-IID learning to propose a neural user-item coupling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recommenders: neural matrix factorization and Google's Wide&Deep network
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world
applications. To achieve accurate MTS forecasting, it is essential to
simultaneously consider both intra- and inter-series relationships among time
series data. However, previous work has typically modeled intra- and
inter-series relationships separately and has disregarded multi-order
interactions present within and between time series data, which can seriously
degrade forecasting accuracy. In this paper, we reexamine intra- and
inter-series relationships from the perspective of mutual information and
accordingly construct a comprehensive relationship learning mechanism tailored
to simultaneously capture the intricate multi-order intra- and inter-series
couplings. Based on the mechanism, we propose a novel deep coupling network for
MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated
to explicitly exploring the multi-order intra- and inter-series relationships
among time series data concurrently, a coupled variable representation module
aimed at encoding diverse variable patterns, and an inference module
facilitating predictions through one forward step. Extensive experiments
conducted on seven real-world datasets demonstrate that our proposed DeepCN
achieves superior performance compared with the state-of-the-art baselines
Non-IID representation learning on complex categorical data
University of Technology Sydney. Faculty of Engineering and Information Technology.Learning complex categorical data requires proper vector or metric representations of the intricate characteristics of that data. Existing methods for categorical data representation usually assume data is independent and identically distributed (IID). However, real-world data is often hierarchically associated with diverse couplings and heterogeneities (i.e., non-IIDness, e.g., various couplings such as value co-occurrences and attribute correlation and dependency, as well as heterogeneities such as heterogeneous distributions or complementary and inconsistent relations). Existing methods either capture only some of these couplings and heterogeneities or simply assume IID data in building their representations.
This thesis aims to deeply understand and effectively represent non-IIDness in categorical data. Specifically, it focuses on (1) modeling heterogeneous couplings within and between attributes in categorical data; (2) disentangling attribute couplings with a mixture of heterogeneous distributions; (3) hierarchically learning heterogeneous couplings; (4) integrating complementary and inconsistent heterogeneous couplings; and (5) adaptively identifying and learning dynamic couplings and heterogeneities.
Accordingly, this thesis proposes (1) a non-IID similarity metrics learning framework to model complex interactions within and between attributes in non-IID categorical data; (2) a decoupled non-IID learning framework to capture and embed heterogeneous distributions in non-IID categorical data with bounded information loss; (3) a heterogeneous metric learning method with hierarchical couplings to learn and integrate the heterogeneous dependencies and distributions in non-IID categorical data into a representation of a similarity metric; (4) an unsupervised heterogeneous coupling learning approach to integrate the complementary and inconsistent heterogeneous couplings in non-IID categorical data; and (5) an unsupervised hierarchical and heterogeneous coupling learning method to learn hierarchical and heterogeneous couplings on dynamic non-IID categorical data.
Theoretical analyses support the effectiveness of the proposed methods and bound the information loss in their generated high-quality representations. Extensive experiments demonstrate that the proposed non-IID representation methods for complex categorical data perform significantly better than state-of-the-art methods in terms of multiple downstream learning tasks and representation-quality evaluation metrics
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on
improving the accuracy of the detection, while largely ignoring the
explainability of the corresponding methods and thus leaving the explanation of
outcomes to practitioners. As anomaly detection algorithms are increasingly
used in safety-critical domains, providing explanations for the high-stakes
decisions made in those domains has become an ethical and regulatory
requirement. Therefore, this work provides a comprehensive and structured
survey on state-of-the-art explainable anomaly detection techniques. We propose
a taxonomy based on the main aspects that characterize each explainable anomaly
detection technique, aiming to help practitioners and researchers find the
explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from
Data (TKDD) for publication (preprint version
Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society
Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with âreversible behaviorâ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of âclassicalâ states that determine molecular dynamics subject to Boltzmann statistics and âsteady-stateâ, metabolic (multi-stable) manifolds, together with âconfigurationâ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the âstandardâ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell âcycleâ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud
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KEYWORDS: Emergence of Life and Human Consciousness;\ud
Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell âcyclingâ; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patientsâ possible improvements of the designs for future clinical trials and cancer treatments. \ud
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Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
In natural language processing (NLP), the context of a word or sentence plays
an essential role. Contextual information such as the semantic representation
of a passage or historical dialogue forms an essential part of a conversation
and a precise understanding of the present phrase or sentence. However, the
standard attention mechanisms typically generate weights using query and key
but ignore context, forming a Bi-Attention framework, despite their great
success in modeling sequence alignment. This Bi-Attention mechanism does not
explicitly model the interactions between the contexts, queries and keys of
target sequences, missing important contextual information and resulting in
poor attention performance. Accordingly, a novel and general triple-attention
(Tri-Attention) framework expands the standard Bi-Attention mechanism and
explicitly interacts query, key, and context by incorporating context as the
third dimension in calculating relevance scores. Four variants of Tri-Attention
are generated by expanding the two-dimensional vector-based additive,
dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the
tensor operations for Tri-Attention. Extensive experiments on three NLP tasks
demonstrate that Tri-Attention outperforms about 30 state-of-the-art
non-attention, standard Bi-Attention, contextual Bi-Attention approaches and
pretrained neural language models1
Human Heuristics for a Team of Mobile Robots
International audienceThis paper is at the crossroad of Cognitive Psychology and AI Robotics. It reports a cross-disciplinary project concerned about implementing human heuristics within autonomous mobile robots. In the following, we address the problem of relying on human-based heuristics to endow a group of mobile robots with the ability to solve problems such as target finding in a labyrinth. Such heuristics may provide an efficient way to explore the environment and to decompose a complex problem into subtasks for which specific heuristics are efficient. We first present a set of experiments conducted with group of humans looking for a target with limited sensing capabilities solving. Then we describe the heuristics extracted from the observation and analysis of their behavior. Finally we implemented these heuristics within khepera-like autonomous mobile robots facing the same tasks. We show that the control architecture can be experimentally validated to some extent thanks to this approach. Index Terms-- Cognition, Autonomous Robotics, Human-centered approach, Heuristics, Multi-agents Problem Solving
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