277 research outputs found
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender
systems for its representation learning capability on user and item embeddings.
However, GCN is vulnerable to noisy and incomplete graphs, which are common in
real world, due to its recursive message propagation mechanism. In the
literature, some work propose to remove the feature transformation during
message propagation, but making it unable to effectively capture the graph
structural features. Moreover, they model users and items in the Euclidean
space, which has been demonstrated to have high distortion when modeling
complex graphs, further degrading the capability to capture the graph
structural features and leading to sub-optimal performance. To this end, in
this paper, we propose a simple yet effective Quaternion-based Graph
Convolution Network (QGCN) recommendation model. In the proposed model, we
utilize the hyper-complex Quaternion space to learn user and item
representations and feature transformation to improve both performance and
robustness. Specifically, we first embed all users and items into the
Quaternion space. Then, we introduce the quaternion embedding propagation
layers with quaternion feature transformation to perform message propagation.
Finally, we combine the embeddings generated at each layer with the mean
pooling strategy to obtain the final embeddings for recommendation. Extensive
experiments on three public benchmark datasets demonstrate that our proposed
QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202
Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation
Quaternion space has brought several benefits over the traditional Euclidean
space: Quaternions (i) consist of a real and three imaginary components,
encouraging richer representations; (ii) utilize Hamilton product which better
encodes the inter-latent interactions across multiple Quaternion components;
and (iii) result in a model with smaller degrees of freedom and less prone to
overfitting. Unfortunately, most of the current recommender systems rely on
real-valued representations in Euclidean space to model either user's long-term
or short-term interests. In this paper, we fully utilize Quaternion space to
model both user's long-term and short-term preferences. We first propose a
QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the
user's long-term intents. Then, we propose a QUaternion-based self-Attentive
Short term user Encoding (QUASE) to learn the user's short-term interests. To
enhance our models' capability, we propose to fuse QUALE and QUASE into one
model, namely QUALSE, by using a Quaternion-based gating mechanism. We further
develop Quaternion-based Adversarial learning along with the Bayesian
Personalized Ranking (QABPR) to improve our model's robustness. Extensive
experiments on six real-world datasets show that our fused QUALSE model
outperformed 11 state-of-the-art baselines, improving 8.43% at HIT@1 and 10.27%
at NDCG@1 on average compared with the best baseline
Continuous Input Embedding Size Search For Recommender Systems
Latent factor models are the most popular backbones for today's recommender
systems owing to their prominent performance. Latent factor models represent
users and items as real-valued embedding vectors for pairwise similarity
computation, and all embeddings are traditionally restricted to a uniform size
that is relatively large (e.g., 256-dimensional). With the exponentially
expanding user base and item catalog in contemporary e-commerce, this design is
admittedly becoming memory-inefficient. To facilitate lightweight
recommendation, reinforcement learning (RL) has recently opened up
opportunities for identifying varying embedding sizes for different
users/items. However, challenged by search efficiency and learning an optimal
RL policy, existing RL-based methods are restricted to highly discrete,
predefined embedding size choices. This leads to a largely overlooked potential
of introducing finer granularity into embedding sizes to obtain better
recommendation effectiveness under a given memory budget. In this paper, we
propose continuous input embedding size search (CIESS), a novel RL-based method
that operates on a continuous search space with arbitrary embedding sizes to
choose from. In CIESS, we further present an innovative random walk-based
exploration strategy to allow the RL policy to efficiently explore more
candidate embedding sizes and converge to a better decision. CIESS is also
model-agnostic and hence generalizable to a variety of latent factor RSs,
whilst experiments on two real-world datasets have shown state-of-the-art
performance of CIESS under different memory budgets when paired with three
popular recommendation models.Comment: To appear in SIGIR'2
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and
thus memory inefficient. This paper proposes a series of lightweight and memory
efficient neural architectures for a potpourri of natural language processing
(NLP) tasks. To this end, our models exploit computation using Quaternion
algebra and hypercomplex spaces, enabling not only expressive inter-component
interactions but also significantly () reduced parameter size due to
lesser degrees of freedom in the Hamilton product. We propose Quaternion
variants of models, giving rise to new architectures such as the Quaternion
attention Model and Quaternion Transformer. Extensive experiments on a battery
of NLP tasks demonstrates the utility of proposed Quaternion-inspired models,
enabling up to reduction in parameter size without significant loss in
performance.Comment: ACL 201
Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications
Mención Internacional en el título de doctorNavigation information is an essential element for the functioning of robotic platforms and
intelligent transportation systems. Among the existing technologies, Global Navigation Satellite
Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for
all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term
for referring to a constellation of satellites which transmit radio signals used primarily for
ranging information. Therefore, the successful operation and deployment of prospective
autonomous systems is subject to our capabilities to support GNSS in the provision of
robust and precise navigational estimates.
GNSS signals enable two types of ranging observations: –code pseudorange, which is a
measure of the time difference between the signal’s emission and reception at the satellite
and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which
measures the beat of the carrier signal and the number of accumulated full carrier cycles.
While code pseudoranges provides an unambiguous measure of the distance between satellites
and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets,
carrier phase measurements present a much higher precision, at the cost of being ambiguous by
an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum
potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase
observations which, in turn, lead to complicated estimation problems.
This thesis deals with the estimation theory behind the provision of carrier phase-based
precise navigation for vehicles traversing scenarios with harsh signal propagation conditions.
Contributions to such a broad topic are made in three directions. First, the ultimate positioning
performance is addressed, by proposing lower bounds on the signal processing realized at the
receiver level and for the mixed real- and integer-valued problem related to carrier phase-based
positioning. Second, multi-antenna configurations are considered for the computation of a
vehicle’s orientation, introducing a new model for the joint position and attitude estimation
problems and proposing new deterministic and recursive estimators based on Lie Theory.
Finally, the framework of robust statistics is explored to propose new solutions to code- and
carrier phase-based navigation, able to deal with outlying impulsive noises.La información de navegación es un elemental fundamental para el funcionamiento de sistemas
de transporte inteligentes y plataformas robóticas. Entre las tecnologías existentes, los
Sistemas Globales de Navegación por Satélite (GNSS) se han consolidado como la piedra
angular para la navegación en exteriores, dando acceso a localización y sincronización temporal
a una escala global, irrespectivamente de la condición meteorológica. GNSS es el término
genérico que define una constelación de satélites que transmiten señales de radio, usadas
primordinalmente para proporcionar información de distancia. Por lo tanto, la operatibilidad y
funcionamiento de los futuros sistemas autónomos pende de nuestra capacidad para explotar
GNSS y estimar soluciones de navegación robustas y precisas.
Las señales GNSS permiten dos tipos de observaciones de alcance: –pseudorangos de
código, que miden el tiempo transcurrido entre la emisión de las señales en los satélites y su
acquisición en la tierra por parte de un receptor; –pseudorangos de fase de portadora, que
miden la fase de la onda sinusoide que portan dichas señales y el número acumulado de ciclos
completos. Los pseudorangos de código proporcionan una medida inequívoca de la distancia
entre los satélites y el receptor, con una precisión de decímetros cuando no se tienen en
cuenta los retrasos atmosféricos y los desfases del reloj. En contraposición, las observaciones
de la portadora son super precisas, alcanzando el milímetro de exactidud, a expensas de ser
ambiguas por un número entero y desconocido de ciclos. Por ende, el alcanzar la máxima
precisión con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo
cual implica unos problemas de estimación de elevada complejidad.
Esta tesis versa sobre la teoría de estimación relacionada con la provisión de navegación
precisa basada en la fase de la portadora, especialmente para vehículos que transitan escenarios
donde las señales no se propagan fácilmente, como es el caso de las ciudades. Para ello,
primero se aborda la máxima efectividad del problema de localización, proponiendo cotas
inferiores para el procesamiento de la señal en el receptor y para el problema de estimación
mixto (es decir, cuando las incógnitas pertenecen al espacio de números reales y enteros). En
segundo lugar, se consideran las configuraciones multiantena para el cálculo de la orientación de un vehículo, presentando un nuevo modelo para la estimación conjunta de posición y
rumbo, y proponiendo estimadores deterministas y recursivos basados en la teoría de Lie. Por
último, se explora el marco de la estadística robusta para proporcionar nuevas soluciones de
navegación precisa, capaces de hacer frente a los ruidos atípicos.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi
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