Dataset condensation, a concept within data-centric learning, efficiently
transfers critical attributes from an original dataset to a synthetic version,
maintaining both diversity and realism. This approach significantly improves
model training efficiency and is adaptable across multiple application areas.
Previous methods in dataset condensation have faced challenges: some incur high
computational costs which limit scalability to larger datasets (e.g., MTT,
DREAM, and TESLA), while others are restricted to less optimal design spaces,
which could hinder potential improvements, especially in smaller datasets
(e.g., SRe2L, G-VBSM, and RDED). To address these limitations, we propose a
comprehensive design framework that includes specific, effective strategies
like implementing soft category-aware matching and adjusting the learning rate
schedule. These strategies are grounded in empirical evidence and theoretical
backing. Our resulting approach, Elucidate Dataset Condensation (EDC),
establishes a benchmark for both small and large-scale dataset condensation. In
our testing, EDC achieves state-of-the-art accuracy, reaching 48.6% on
ImageNet-1k with a ResNet-18 model at an IPC of 10, which corresponds to a
compression ratio of 0.78%. This performance exceeds those of SRe2L, G-VBSM,
and RDED by margins of 27.3%, 17.2%, and 6.6%, respectively.

通过设计一个综合的框架，实现了软分类感知匹配和调整学习速率的策略，从而在小型和大型数据集压缩上建立了基准，显著提高了模型训练效率。

揭示数据集精简的设计空间

Elucidating the Design Space of Dataset Condensation

Large Language Models (LLMs) went from non-existent to ubiquitous in the
machine learning discourse within a few years. Due to the fast pace of the
field, it is difficult to identify the remaining challenges and already
fruitful application areas. In this paper, we aim to establish a systematic set
of open problems and application successes so that ML researchers can
comprehend the field's current state more quickly and become productive.

大型语言模型已成为机器学习领域中无所不在的话题，但是由于该领域的快速发展，很难确定剩余的挑战和已经成功应用的领域。因此，本文旨在建立一个系统的开放问题和应用成功集合，以帮助机器学习研究人员更快地了解该领域的现状并提高效率。

大语言模型的挑战与应用

Challenges and Applications of Large Language Models

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the
strong force, is expected to begin commissioning its first experiments in 2028.
This is an opportune time for artificial intelligence (AI) to be included from
the start at this facility and in all phases that lead up to the experiments.
The second annual workshop organized by the AI4EIC working group, which
recently took place, centered on exploring all current and prospective
application areas of AI for the EIC. This workshop is not only beneficial for
the EIC, but also provides valuable insights for the newly established ePIC
collaboration at EIC. This paper summarizes the different activities and R&D
projects covered across the sessions of the workshop and provides an overview
of the goals, approaches and strategies regarding AI/ML in the EIC community,
as well as cutting-edge techniques currently studied in other experiments.

本研究论文总结了 AI/ML 在 EIC 社区的目标、方法和策略，以及在其他实验中研究的尖端技术，包括在第二次年度工作坊上讨论的不同活动和研发项目，并提供了有关 AI 在 EIC 各个方面的展望。

电子离子对撞机的人工智能应用（AI4EIC）

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

Graphs are ubiquitous in encoding relational information of real-world
objects in many domains. Graph generation, whose purpose is to generate new
graphs from a distribution similar to the observed graphs, has received
increasing attention thanks to the recent advances of deep learning models. In
this paper, we conduct a comprehensive review on the existing literature of
deep graph generation from a variety of emerging methods to its wide
application areas. Specifically, we first formulate the problem of deep graph
generation and discuss its difference with several related graph learning
tasks. Secondly, we divide the state-of-the-art methods into three categories
based on model architectures and summarize their generation strategies.
Thirdly, we introduce three key application areas of deep graph generation.
Lastly, we highlight challenges and opportunities in the future study of deep
graph generation. We hope that our survey will be useful for researchers and
practitioners who are interested in this exciting and rapidly-developing field.

该论文对深度图生成的现有文献进行了综述，并从模型架构、应用领域等方面进行了细分，总结了图生成的关键策略，同时阐明了未来深度图生成研究的挑战和机遇。