229 research outputs found
Frugal Optimization for Cost-related Hyperparameters
The increasing demand for democratizing machine learning algorithms calls for
hyperparameter optimization (HPO) solutions at low cost. Many machine learning
algorithms have hyperparameters which can cause a large variation in the
training cost. But this effect is largely ignored in existing HPO methods,
which are incapable to properly control cost during the optimization process.
To address this problem, we develop a new cost-frugal HPO solution. The core of
our solution is a simple but new randomized direct-search method, for which we
prove a convergence rate of and an
-approximation guarantee on the total cost. We provide
strong empirical results in comparison with state-of-the-art HPO methods on
large AutoML benchmarks.Comment: 29 pages (including supplementary appendix
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at
https://github.com/EagleW/PaperRobo
Urban nighttime leisure space mapping with nighttime light images and POI data
Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km(2), which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures
Quantification of the relationship between sea surface roughness and the size of the glistening zone for GNSS-R
A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy (r=0.7
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to
reach a particular goal, which is an essential task to assist robots or humans
in performing stereotypical activities. An important aspect of this process is
the ability to capture historical states visually, which provides detailed
information that is not covered by text and will guide subsequent steps.
Therefore, we propose a new task, Multimedia Generative Script Learning, to
generate subsequent steps by tracking historical states in both text and vision
modalities, as well as presenting the first benchmark containing 5,652 tasks
and 79,089 multimedia steps. This task is challenging in three aspects: the
multimedia challenge of capturing the visual states in images, the induction
challenge of performing unseen tasks, and the diversity challenge of covering
different information in individual steps. We propose to encode visual state
changes through a selective multimedia encoder to address the multimedia
challenge, transfer knowledge from previously observed tasks using a
retrieval-augmented decoder to overcome the induction challenge, and further
present distinct information at each step by optimizing a diversity-oriented
contrastive learning objective. We define metrics to evaluate both generation
and inductive quality. Experiment results demonstrate that our approach
significantly outperforms strong baselines.Comment: 21 pages, Accepted by Findings of the Association for Computational
Linguistics: ACL 2023, Code and Resources at
https://github.com/EagleW/Multimedia-Generative-Script-Learnin
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