Online deep learning solves the problem of learning from streams of data,
reconciling two opposing objectives: learn fast and learn deep. Existing work
focuses almost exclusively on exploring pure deep learning solutions, which are
much better suited to handle the "deep" than the "fast" part of the online
learning equation. In our work, we propose a different paradigm, based on a
hybrid multilearner approach. First, we develop a fast online logistic
regression learner. This learner does not rely on backpropagation. Instead, it
uses closed form recursive updates of model parameters, handling the fast
learning part of the online learning problem. We then analyze the existing
online deep learning theory and show that the widespread ODL approach,
currently operating at complexity $O(L^2)$ in terms of the number of layers
$L$, can be equivalently implemented in $O(L)$ complexity. This further leads
us to the cascaded multilearner design, in which multiple shallow and deep
learners are co-trained to solve the online learning problem in a cooperative,
synergistic fashion. We show that this approach achieves state-of-the-art
results on common online learning datasets, while also being able to handle
missing features gracefully. Our code is publicly available at
this https URL

在线深度学习采用混合多学习器方法，实现快速学习和深度学习的有效平衡，并通过协同学习解决在线学习问题，能够优雅地处理缺失特征，实现了最先进的结果。

MODL：多学习器在线深度学习

MODL: Multilearner Online Deep Learning

Reinforcement-learning agents seek to maximize a reward signal through
environmental interactions. As humans, our contribution to the learning process
is through designing the reward function. Like programmers, we have a behavior
in mind and have to translate it into a formal specification, namely rewards.
In this work, we consider the reward-design problem in tasks formulated as
reaching desirable states and avoiding undesirable states. To start, we propose
a strict partial ordering of the policy space. We prefer policies that reach
the good states faster and with higher probability while avoiding the bad
states longer. Next, we propose an environment-independent tiered reward
structure and show it is guaranteed to induce policies that are Pareto-optimal
according to our preference relation. Finally, we empirically evaluate tiered
reward functions on several environments and show they induce desired behavior
and lead to fast learning.

考虑在任务中以达到期望状态和避免不良状态的形式下，建议采用严格偏序的政策空间、环境独立的层次化奖励结构以及 Pareto 最优性的奖励函数设计，经实验证明其具有期望行为和快速学习的能力。

使用层次奖励函数指定行为偏好

Specifying Behavior Preference with Tiered Reward Functions

Reservoir computing is a computational framework suited for
temporal/sequential data processing. It is derived from several recurrent
neural network models, including echo state networks and liquid state machines.
A reservoir computing system consists of a reservoir for mapping inputs into a
high-dimensional space and a readout for pattern analysis from the
high-dimensional states in the reservoir. The reservoir is fixed and only the
readout is trained with a simple method such as linear regression and
classification. Thus, the major advantage of reservoir computing compared to
other recurrent neural networks is fast learning, resulting in low training
cost. Another advantage is that the reservoir without adaptive updating is
amenable to hardware implementation using a variety of physical systems,
substrates, and devices. In fact, such physical reservoir computing has
attracted increasing attention in diverse fields of research. The purpose of
this review is to provide an overview of recent advances in physical reservoir
computing by classifying them according to the type of the reservoir. We
discuss the current issues and perspectives related to physical reservoir
computing, in order to further expand its practical applications and develop
next-generation machine learning systems.

该综述通过将物理沉积池按类型分类，概述了近期物理沉积池计算的进展，并讨论了与物理沉积池计算相关的当前问题和前景，以进一步扩展其实际应用和开发下一代机器学习系统。