A new Genetic Programming variant called Liquid State Genetic Programming
(LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic
memory for storing the inputs (the liquid) and a Genetic Programming technique
used for the problem solving part. Several numerical experiments with LSGP are
performed by using several benchmarking problems. Numerical experiments show
that LSGP performs similarly and sometimes even better than standard Genetic
Programming for the considered test problems.

提出了一种名为液态遗传规划（LSGP）的新型遗传规划变体，LSGP 是一种混合方法，结合了用于存储输入（液体）的动态内存和用于问题求解部分的遗传规划技术。通过使用多个基准测试问题进行了数值实验，实验结果表明，在考虑的测试问题中，LSGP 的性能与标准遗传规划类似，有时甚至更好。

液态遗传编程

Liquid State Genetic Programming

The sparsity of extrinsic rewards poses a serious challenge for reinforcement
learning (RL). Currently, many efforts have been made on curiosity which can
provide a representative intrinsic reward for effective exploration. However,
the challenge is still far from being solved. In this paper, we present a novel
curiosity for RL, named DyMeCu, which stands for Dynamic Memory-based
Curiosity. Inspired by human curiosity and information theory, DyMeCu consists
of a dynamic memory and dual online learners. The curiosity arouses if
memorized information can not deal with the current state, and the information
gap between dual learners can be formulated as the intrinsic reward for agents,
and then such state information can be consolidated into the dynamic memory.
Compared with previous curiosity methods, DyMeCu can better mimic human
curiosity with dynamic memory, and the memory module can be dynamically grown
based on a bootstrap paradigm with dual learners. On multiple benchmarks
including DeepMind Control Suite and Atari Suite, large-scale empirical
experiments are conducted and the results demonstrate that DyMeCu outperforms
competitive curiosity-based methods with or without extrinsic rewards. We will
release the code to enhance reproducibility.

提出一种新的基于动态记忆和信息理论的强化学习探索算法 DyMeCu，并在多个基准测试中展示其优越性，能够有效模拟人类好奇心和实现动态记忆。

基于动态内存的好奇心：探索的引导方法

Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration

In medical imaging, technical progress or changes in diagnostic procedures
lead to a continuous change in image appearance. Scanner manufacturer,
reconstruction kernel, dose, other protocol specific settings or administering
of contrast agents are examples that influence image content independent of the
scanned biology. Such domain and task shifts limit the applicability of machine
learning algorithms in the clinical routine by rendering models obsolete over
time. Here, we address the problem of data shifts in a continuous learning
scenario by adapting a model to unseen variations in the source domain while
counteracting catastrophic forgetting effects. Our method uses a dynamic memory
to facilitate rehearsal of a diverse training data subset to mitigate
forgetting. We evaluated our approach on routine clinical CT data obtained with
two different scanner protocols and synthetic classification tasks. Experiments
show that dynamic memory counters catastrophic forgetting in a setting with
multiple data shifts without the necessity for explicit knowledge about when
these shifts occur.

本研究介绍了一种使用动态内存来适应医学影像中不断变化的数据漂移及应对灾难性遗忘效应的机器学习算法，并在两种不同扫描协议的临床 CT 数据和合成分类任务中进行了实验验证。实验表明，动态内存能有效对抗多个数据漂移设置下的灾难性遗忘，并不需要显式的知识来识别这些漂移发生的时间。

在连续学习中通过动态内存缓解灾难性遗忘

Dynamic memory to alleviate catastrophic forgetting in continuous  learning settings

Action anticipation and forecasting in videos do not require a hat-trick, as
far as there are signs in the context to foresee how actions are going to be
deployed. Capturing these signs is hard because the context includes the past.
We propose an end-to-end network for action anticipation and forecasting with
memory, to both anticipate the current action and foresee the next one.
Experiments on action sequence datasets show excellent results indicating that
training on histories with a dynamic memory can significantly improve
forecasting performance.

提出一种基于记忆的端到端网络进行行为预测与预测，表明使用动态内存训练历史可以显着提高预测性能。