300 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Modeling Events and Interactions through Temporal Processes -- A Survey
In real-world scenario, many phenomena produce a collection of events that
occur in continuous time. Point Processes provide a natural mathematical
framework for modeling these sequences of events. In this survey, we
investigate probabilistic models for modeling event sequences through temporal
processes. We revise the notion of event modeling and provide the mathematical
foundations that characterize the literature on the topic. We define an
ontology to categorize the existing approaches in terms of three families:
simple, marked, and spatio-temporal point processes. For each family, we
systematically review the existing approaches based based on deep learning.
Finally, we analyze the scenarios where the proposed techniques can be used for
addressing prediction and modeling aspects.Comment: Image replacement
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to
extend the in-domain model to the distinctive target domains where the data
distributions differ. Most prior works focus on capturing the inter-domain
transferability but largely overlook rich intra-domain structures, which
empirically results in even worse discriminability. In this work, we introduce
a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The
core of our method is briefly condensed as follows: (i)-by casting the DA
problem to graph primitives, SPA composes a coarse graph alignment mechanism
with a novel spectral regularizer towards aligning the domain graphs in
eigenspaces; (ii)-we further develop a fine-grained message propagation module
-- upon a novel neighbor-aware self-training mechanism -- in order for enhanced
discriminability in the target domain. On standardized benchmarks, the
extensive experiments of SPA demonstrate that its performance has surpassed the
existing cutting-edge DA methods. Coupled with dense model analysis, we
conclude that our approach indeed possesses superior efficacy, robustness,
discriminability, and transferability. Code and data are available at:
https://github.com/CrownX/SPA.Comment: NeurIPS 2023 camera read
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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