Video stabilization is a longstanding computer vision problem, particularly
pixel-level synthesis solutions for video stabilization which synthesize full
frames add to the complexity of this task. These techniques aim to stabilize
videos by synthesizing full frames while enhancing the stability of the
considered video. This intensifies the complexity of the task due to the
distinct mix of unique motion profiles and visual content present in each video
sequence, making robust generalization with fixed parameters difficult. In our
study, we introduce a novel approach to enhance the performance of pixel-level
synthesis solutions for video stabilization by adapting these models to
individual input video sequences. The proposed adaptation exploits low-level
visual cues accessible during test-time to improve both the stability and
quality of resulting videos. We highlight the efficacy of our methodology of
"test-time adaptation" through simple fine-tuning of one of these models,
followed by significant stability gain via the integration of meta-learning
techniques. Notably, significant improvement is achieved with only a single
adaptation step. The versatility of the proposed algorithm is demonstrated by
consistently improving the performance of various pixel-level synthesis models
for video stabilization in real-world scenarios.

通过采用测试时间自适应方法，我们提出了一种新颖的方法来提升视频稳定的像素级合成解决方案的性能，该方法利用低级视觉线索改善结果视频的稳定性和质量，通过元学习技术显著提高了稳定性，仅需一个自适应步骤。该算法在实际场景中提高了各种像素级合成模型用于视频稳定的性能。

利用元学习改善全画面视频稳定化

Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

Recommender systems have become fundamental building blocks of modern online
products and services, and have a substantial impact on user experience. In the
past few years, deep learning methods have attracted a lot of research, and are
now heavily used in modern real-world recommender systems. Nevertheless,
dealing with recommendations in the cold-start setting, e.g., when a user has
done limited interactions in the system, is a problem that remains far from
solved. Meta-learning techniques, and in particular optimization-based
meta-learning, have recently become the most popular approaches in the academic
research literature for tackling the cold-start problem in deep learning models
for recommender systems. However, current meta-learning approaches are not
practical for real-world recommender systems, which have billions of users and
items, and strict latency requirements. In this paper we show that it is
possible to obtaining similar, or higher, performance on commonly used
benchmarks for the cold-start problem without using meta-learning techniques.
In more detail, we show that, when tuned correctly, standard and widely adopted
deep learning models perform just as well as newer meta-learning models. We
further show that an extremely simple modular approach using common
representation learning techniques, can perform comparably to meta-learning
techniques specifically designed for the cold-start setting while being much
more easily deployable in real-world applications.

深度学习方法在冷启动问题中表现良好，与元学习技术相比，常用的深度学习模型和普遍采用的表示学习技术能够在常见的冷启动问题基准上提供类似或更好的性能，并且更易于实际应用。

元学习对推荐系统中的冷启动问题是否是正确的方法？

Is Meta-Learning the Right Approach for the Cold-Start Problem in  Recommender Systems?

Objective: The artificial pancreas (AP) has shown promising potential in
achieving closed-loop glucose control for individuals with type 1 diabetes
mellitus (T1DM). However, designing an effective control policy for the AP
remains challenging due to the complex physiological processes, delayed insulin
response, and inaccurate glucose measurements. While model predictive control
(MPC) offers safety and stability through the dynamic model and safety
constraints, it lacks individualization and is adversely affected by
unannounced meals. Conversely, deep reinforcement learning (DRL) provides
personalized and adaptive strategies but faces challenges with distribution
shifts and substantial data requirements. Methods: We propose a hybrid control
policy for the artificial pancreas (HyCPAP) to address the above challenges.
HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the
strengths of both policies while compensating for their respective limitations.
To facilitate faster deployment of AP systems in real-world settings, we
further incorporate meta-learning techniques into HyCPAP, leveraging previous
experience and patient-shared knowledge to enable fast adaptation to new
patients with limited available data. Results: We conduct extensive experiments
using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our
approaches achieve the highest percentage of time spent in the desired
euglycemic range and the lowest occurrences of hypoglycemia. Conclusion: The
results clearly demonstrate the superiority of our methods for closed-loop
glucose management in individuals with T1DM. Significance: The study presents
novel control policies for AP systems, affirming the great potential of
proposed methods for efficient closed-loop glucose control.

本文提出了一种 HyCPAP 的混合控制策略，将 MPC 和集成 DRL 策略相结合，利用两种策略的优势并补偿各自的限制，并进一步将元学习技术纳入 HyCPAP 中，以实现对 T1DM 患者的快速个性化适应。结果表明，我们的方法在闭环葡萄糖管理方面表现优越。

通过集成深度强化学习的混合控制策略应用于人工胰腺

Hybrid Control Policy for Artificial Pancreas via Ensemble Deep  Reinforcement Learning

Deep neural networks can achieve great successes when presented with large
data sets and sufficient computational resources. However, their ability to
learn new concepts quickly is limited. Meta-learning is one approach to address
this issue, by enabling the network to learn how to learn. The field of Deep
Meta-Learning advances at great speed, but lacks a unified, in-depth overview
of current techniques. With this work, we aim to bridge this gap. After
providing the reader with a theoretical foundation, we investigate and
summarize key methods, which are categorized into i)~metric-, ii)~model-, and
iii)~optimization-based techniques. In addition, we identify the main open
challenges, such as performance evaluations on heterogeneous benchmarks, and
reduction of the computational costs of meta-learning.

本论文旨在提供关于 Deep Meta-Learning 的理论基础以及总结关键的度量、模型和优化技术，同时也指出了性能评估和计算成本等主要挑战。