293 research outputs found
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Stochastic Gradient Descent (SGD) is one of the simplest and most popular
algorithms in modern statistical and machine learning due to its computational
and memory efficiency. Various averaging schemes have been proposed to
accelerate the convergence of SGD in different settings. In this paper, we
explore a general averaging scheme for SGD. Specifically, we establish the
asymptotic normality of a broad range of weighted averaged SGD solutions and
provide asymptotically valid online inference approaches. Furthermore, we
propose an adaptive averaging scheme that exhibits both optimal statistical
rate and favorable non-asymptotic convergence, drawing insights from the
optimal weight for the linear model in terms of non-asymptotic mean squared
error (MSE)
Focus-Driven Contrastive Learniang for Medical Question Summarization
Automatic medical question summarization can significantly help the system to
understand consumer health questions and retrieve correct answers. The Seq2Seq
model based on maximum likelihood estimation (MLE) has been applied in this
task, which faces two general problems: the model can not capture well question
focus and and the traditional MLE strategy lacks the ability to understand
sentence-level semantics. To alleviate these problems, we propose a novel
question focus-driven contrastive learning framework (QFCL). Specially, we
propose an easy and effective approach to generate hard negative samples based
on the question focus, and exploit contrastive learning at both encoder and
decoder to obtain better sentence level representations. On three medical
benchmark datasets, our proposed model achieves new state-of-the-art results,
and obtains a performance gain of 5.33, 12.85 and 3.81 points over the baseline
BART model on three datasets respectively. Further human judgement and detailed
analysis prove that our QFCL model learns better sentence representations with
the ability to distinguish different sentence meanings, and generates
high-quality summaries by capturing question focus.Comment: Accepted by COLING 2022, long pape
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
A reasonable and balanced diet is essential for maintaining good health. With
the advancements in deep learning, automated nutrition estimation method based
on food images offers a promising solution for monitoring daily nutritional
intake and promoting dietary health. While monocular image-based nutrition
estimation is convenient, efficient, and economical, the challenge of limited
accuracy remains a significant concern. To tackle this issue, we proposed
DPF-Nutrition, an end-to-end nutrition estimation method using monocular
images. In DPF-Nutrition, we introduced a depth prediction module to generate
depth maps, thereby improving the accuracy of food portion estimation.
Additionally, we designed an RGB-D fusion module that combined monocular images
with the predicted depth information, resulting in better performance for
nutrition estimation. To the best of our knowledge, this was the pioneering
effort that integrated depth prediction and RGB-D fusion techniques in food
nutrition estimation. Comprehensive experiments performed on Nutrition5k
evaluated the effectiveness and efficiency of DPF-Nutrition
The Rational Agent Benchmark for Data Visualization
Understanding how helpful a visualization is from experimental results is
difficult because the observed performance is confounded with aspects of the
study design, such as how useful the information that is visualized is for the
task. We develop a rational agent framework for designing and interpreting
visualization experiments. Our framework conceives two experiments with the
same setup: one with behavioral agents (human subjects), the other one with a
hypothetical rational agent. A visualization is evaluated by comparing the
expected performance of behavioral agents to that of rational agent under
different assumptions. Using recent visualization decision studies from the
literature, we demonstrate how the framework can be used to pre-experimentally
evaluate the experiment design by bounding the expected improvement in
performance from having access to visualizations, and post-experimentally to
deconfound errors of information extraction from errors of optimization, among
other analyses
Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment
This paper designs a new and scientific environmental quality assessment
method, and takes Saihan dam as an example to explore the environmental
improvement degree to the local and Beijing areas. AHP method is used to assign
values to each weight 7 primary indicators and 21 secondary indicators were
used to establish an environmental quality assessment model. The conclusion
shows that after the establishment of Saihan dam, the local environmental
quality has been improved by 7 times, and the environmental quality in Beijing
has been improved by 13%. Then the future environmental index is predicted.
Finally the Spearson correlation coefficient is analyzed, and it is proved that
correlation is 99% when the back-propagation algorithm is used to test and
prove that the error is little
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