1,419 research outputs found
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
In this work, we develop a simple algorithm for semi-supervised regression.
The key idea is to use the top eigenfunctions of integral operator derived from
both labeled and unlabeled examples as the basis functions and learn the
prediction function by a simple linear regression. We show that under
appropriate assumptions about the integral operator, this approach is able to
achieve an improved regression error bound better than existing bounds of
supervised learning. We also verify the effectiveness of the proposed algorithm
by an empirical study.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Global dynamics in a chemotaxis model describing tumor angiogenesis with/without mitosis in any dimensions
In this work, we study the Neumann initial boundary value problem for a
three-component chemotaxis model in any dimensional bounded and smooth domains;
this model is used to describe the branching of capillary sprouts during
angiogenesis. First, we find three qualitatively simple sufficient conditions
for qualitative global boundedness, and then, we establish two types of global
stability for bounded solutions in qualitative ways. As a consequence of our
findings, the underlying system without chemotaxis and the effect of ECs
mitosis can not give rise to pattern formations. Our findings quantify and
extend significantly previous studies, which are set in lower dimensional
convex domains and are with no qualitative information.Comment: 43 pages, under review in a journa
Configuration Tool and Experimental Platform for Pointing Devices
In user studies of human-computer interaction, experiments on new devices and techniques are often made on experiment software, which is developed separately for each device and technique. A systematic experimental platform, capable of running experiments on a number of technologies, would facilitate the design and implementation of such experiments. To do this, a configurable framework was created to allow relative pointing and absolute pointing input to be enhanced with adaptive pointing and smoothed pointing techniques. This thesis discusses both the internals of the framework as well as how a platform is developed based on the framework. Additionally, two calibration modules were designed to transform the relative pointing input to absolute pointing and obtain the necessary parameters which will be applied in smoothed pointing. As a part of the deployment, the experiment module was made to provide a platform which allowed the enhanced pointing experience to be evaluated and generated proper output according to the results of the experiment task.
One key achievement presented in this thesis is that the relative pointing devices are integrable with adaptive pointing and smoothed pointing which support for absolute pointing devices in general. Another key result presented in this thesis is that the configurable framework based experimental platform provides proper functions which meet the demands of professional pointing evaluation.
ACM Computing Classification System (CCS):
I.4.1 [Digitization and Image Capture]: Camera calibration,
I.4.3 [Enhancement]: Smoothing,
I.4.8 [Scene Analysis]: Trackin
HOW CAN PRODUCT TEXT SNIPPETS BENEFIT FROM ONLINE CUSTOMER REVIEWS?
Product text snippets should highlight the product features that are appealing to customers. Nevertheless, the features in current product snippets mainly are often decided based on the understanding of vendors or advertisers, and may fail to contain the features appealing to customers. This paper investigates how product text snippets generation can benefit from online customer reviews. In doing so, an automated method is designed, in which features and the opinions are extracted from online reviews, and are further used for product text snippet generation. To verify the effectiveness of the proposed method, we conduct two experiments and the results show that the extracted features and the snippet are effective in inviting potential customers, compared with the baseline ones. Experimental results demonstrate that 1) the extracted features are more appealing to customers; and 2) the snippets generated based on the extracted features are more likely to be clicked
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Representation learning on networks aims to derive a meaningful vector
representation for each node, thereby facilitating downstream tasks such as
link prediction, node classification, and node clustering. In heterogeneous
text-rich networks, this task is more challenging due to (1) presence or
absence of text: Some nodes are associated with rich textual information, while
others are not; (2) diversity of types: Nodes and edges of multiple types form
a heterogeneous network structure. As pretrained language models (PLMs) have
demonstrated their effectiveness in obtaining widely generalizable text
representations, a substantial amount of effort has been made to incorporate
PLMs into representation learning on text-rich networks. However, few of them
can jointly consider heterogeneous structure (network) information as well as
rich textual semantic information of each node effectively. In this paper, we
propose Heterformer, a Heterogeneous Network-Empowered Transformer that
performs contextualized text encoding and heterogeneous structure encoding in a
unified model. Specifically, we inject heterogeneous structure information into
each Transformer layer when encoding node texts. Meanwhile, Heterformer is
capable of characterizing node/edge type heterogeneity and encoding nodes with
or without texts. We conduct comprehensive experiments on three tasks (i.e.,
link prediction, node classification, and node clustering) on three large-scale
datasets from different domains, where Heterformer outperforms competitive
baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer
Using simulated Tianqin gravitational wave data and electromagnetic wave data to study the coincidence problem and Hubble tension problem
In this paper, we use electromagnetic wave data (H0LiCOW, , SNe) and
gravitational wave data (Tianqin) to constrain the interacting dark energy
(IDE) model and investigate the Hubble tension problem and coincidences
problem. By combining these four kinds of data (Tianqin+H0LiCOW+SNe+), we
obtained the parameter values at the confidence interval of :
, ,
, and . According
to our results, the best valve of show that the Hubble tension problem
can be alleviated to some extent. In addition, the of which the center value indicates the
coincidence problem is slightly alleviated. However, the is
still within the error range which indicates the CDM model
is still the model which is in best agreement with the observational data at
present. Finally, we compare the constraint results of electromagnetic wave and
gravitational wave on the model parameters and find that the constraint effect
of electromagnetic wave data on model parameters is better than that of
simulated Tianqin gravitational wave data.Comment: The article has been accepted by Chinese Physics
"Why Should I Review This Paper?" Unifying Semantic, Topic, and Citation Factors for Paper-Reviewer Matching
As many academic conferences are overwhelmed by a rapidly increasing number
of paper submissions, automatically finding appropriate reviewers for each
submission becomes a more urgent need than ever. Various factors have been
considered by previous attempts on this task to measure the expertise relevance
between a paper and a reviewer, including whether the paper is semantically
close to, shares topics with, and cites previous papers of the reviewer.
However, the majority of previous studies take only one of these factors into
account, leading to an incomprehensive evaluation of paper-reviewer relevance.
To bridge this gap, in this paper, we propose a unified model for
paper-reviewer matching that jointly captures semantic, topic, and citation
factors. In the unified model, a contextualized language model backbone is
shared by all factors to learn common knowledge, while instruction tuning is
introduced to characterize the uniqueness of each factor by producing
factor-aware paper embeddings. Experiments on four datasets (one of which is
newly contributed by us) across different fields, including machine learning,
computer vision, information retrieval, and data mining, consistently validate
the effectiveness of our proposed UniPR model in comparison with
state-of-the-art paper-reviewer matching methods and scientific pre-trained
language models
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
A key challenge of modern machine learning systems is to achieve
Out-of-Distribution (OOD) generalization -- generalizing to target data whose
distribution differs from that of source data. Despite its significant
importance, the fundamental question of ``what are the most effective
algorithms for OOD generalization'' remains open even under the standard
setting of covariate shift. This paper addresses this fundamental question by
proving that, surprisingly, classical Maximum Likelihood Estimation (MLE)
purely using source data (without any modification) achieves the minimax
optimality for covariate shift under the well-specified setting. That is, no
algorithm performs better than MLE in this setting (up to a constant factor),
justifying MLE is all you need. Our result holds for a very rich class of
parametric models, and does not require any boundedness condition on the
density ratio. We illustrate the wide applicability of our framework by
instantiating it to three concrete examples -- linear regression, logistic
regression, and phase retrieval. This paper further complement the study by
proving that, under the misspecified setting, MLE is no longer the optimal
choice, whereas Maximum Weighted Likelihood Estimator (MWLE) emerges as minimax
optimal in certain scenarios
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is
a pressing need to tag each paper with fine-grained topics so that researchers
can track their interested fields of study rather than drowning in the whole
literature. Scientific literature tagging is beyond a pure multi-label text
classification task because papers on the Web are prevalently accompanied by
metadata information such as venues, authors, and references, which may serve
as additional signals to infer relevant tags. Although there have been studies
making use of metadata in academic paper classification, their focus is often
restricted to one or two scientific fields (e.g., computer science and
biomedicine) and to one specific model. In this work, we systematically study
the effect of metadata on scientific literature tagging across 19 fields. We
select three representative multi-label classifiers (i.e., a bag-of-words
model, a sequence-based model, and a pre-trained language model) and explore
their performance change in scientific literature tagging when metadata are fed
to the classifiers as additional features. We observe some ubiquitous patterns
of metadata's effects across all fields (e.g., venues are consistently
beneficial to paper tagging in almost all cases), as well as some unique
patterns in fields other than computer science and biomedicine, which are not
explored in previous studies.Comment: 11 pages; Accepted to WWW 202
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