246 research outputs found
Coreset selection can accelerate quantum machine learning models with provable generalization
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures
in the realm of quantum machine learning, poised to leverage the nascent
capabilities of near-term quantum computers to surmount classical machine
learning challenges. Nonetheless, the training efficiency challenge poses a
limitation on both QNNs and quantum kernels, curbing their efficacy when
applied to extensive datasets. To confront this concern, we present a unified
approach: coreset selection, aimed at expediting the training of QNNs and
quantum kernels by distilling a judicious subset from the original training
dataset. Furthermore, we analyze the generalization error bounds of QNNs and
quantum kernels when trained on such coresets, unveiling the comparable
performance with those training on the complete original dataset. Through
systematic numerical simulations, we illuminate the potential of coreset
selection in expediting tasks encompassing synthetic data classification,
identification of quantum correlations, and quantum compiling. Our work offers
a useful way to improve diverse quantum machine learning models with a
theoretical guarantee while reducing the training cost.Comment: 25 pages, 7 figure
Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
The success of most advanced facial expression recognition works relies
heavily on large-scale annotated datasets. However, it poses great challenges
in acquiring clean and consistent annotations for facial expression datasets.
On the other hand, self-supervised contrastive learning has gained great
popularity due to its simple yet effective instance discrimination training
strategy, which can potentially circumvent the annotation issue. Nevertheless,
there remain inherent disadvantages of instance-level discrimination, which are
even more challenging when faced with complicated facial representations. In
this paper, we revisit the use of self-supervised contrastive learning and
explore three core strategies to enforce expression-specific representations
and to minimize the interference from other facial attributes, such as identity
and face styling. Experimental results show that our proposed method
outperforms the current state-of-the-art self-supervised learning methods, in
terms of both categorical and dimensional facial expression recognition tasks.Comment: Accepted to BMVC 202
Holographic Einstein Ring of a Charged AdS Black Hole
Taking into account that the real quantum materials are engineered
generically at a finite chemical potential, we investigate the Einstein ring
structure for the lensed response of the complex scalar field as a probe wave
on the charged AdS black hole in the context of AdS/CFT. On the one hand, we
find that the resulting Einstein ring radius has no variation with the chemical
potential, which is similar to the behavior for the weakly interacting quantum
system. On the other hand, not only can such a ring exist well within the
screen, but also the temperature dependence of its radius exhibits a distinct
feature in the sense that it displays an appreciable increase at low
temperatures while the ring keeps unchanged right at the edge of the screen for
the weakly interacting system. Note that such a Einstein ring emerges in the
large frequencies and can be well captured by the photon sphere away from the
black hole horizon in the geometric optics approximation, thus such a distinct
feature may be regarded as a universal behavior associated with the high energy
modes of the strongly coupled system which has a gravity dual.Comment: totally new version with the authors added and perspectives
sharpened, 11 figures, to appear in JHE
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
The multi-answer phenomenon, where a question may have multiple answers
scattered in the document, can be well handled by humans but is challenging
enough for machine reading comprehension (MRC) systems. Despite recent progress
in multi-answer MRC, there lacks a systematic analysis of how this phenomenon
arises and how to better address it. In this work, we design a taxonomy to
categorize commonly-seen multi-answer MRC instances, with which we inspect
three multi-answer datasets and analyze where the multi-answer challenge comes
from. We further analyze how well different paradigms of current multi-answer
MRC models deal with different types of multi-answer instances. We find that
some paradigms capture well the key information in the questions while others
better model the relationship between questions and contexts. We thus explore
strategies to make the best of the strengths of different paradigms.
Experiments show that generation models can be a promising platform to
incorporate different paradigms. Our annotations and code are released for
further research.Comment: Findings of ACL 202
Time-varying resonant mass at collider and beam dump experiments
A new particle usually manifests itself as a single resonant peak located at its mass. We propose if the new particle mass is time-varying due to environmental effects, then its mass spectrum typically has a novel double-peak feature. A representative model is the kinetic mixing dark photon interacting with an ultralight complex scalar dark matter charged under U(1)\u27. We reanalyze the existing experiments, showing the constraints on such a model are drastically weakened than those on the traditional single-peak resonance model, due to the reduction of the luminosity exposure in each resonant mass bin. Consequently, for mass around tens of MeV, the muon gμ -2 solution from the kinetic mixing dark photon becomes viable again. The scenario can be further tested by reanalyzing the existing data with timing information included
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