Many learning tasks in machine learning can be viewed as taking a gradient
step towards minimizing the average loss of a batch of examples in each
training iteration. When noise is prevalent in the data, this uniform treatment
of examples can lead to overfitting to noisy examples with larger loss values
and result in poor generalization. Inspired by the expert setting in on-line
learning, we present a flexible approach to learning from noisy examples.
Specifically, we treat each training example as an expert and maintain a
distribution over all examples. We alternate between updating the parameters of
the model using gradient descent and updating the example weights using the
exponentiated gradient update. Unlike other related methods, our approach
handles a general class of loss functions and can be applied to a wide range of
noise types and applications. We show the efficacy of our approach for multiple
learning settings, namely noisy principal component analysis and a variety of
noisy classification problems.

研究了在机器学习中，每个训练周期都是将梯度步骤视为朝着最小化每个批次的例子的平均损失的方向，其中噪声会导致过度拟合到损失值较大的噪声样本，提出了一种使用指数梯度更新的加权学习方法，适用于一系列噪声类型和应用场景的损失函数。

标签噪声下和更多情况下的指数梯度加权鲁棒训练

Exponentiated Gradient Reweighting for Robust Training Under Label Noise  and Beyond

We present a deep, bidirectional, recurrent framework for cleaning noisy and
incomplete motion capture data. It exploits temporal coherence and joint
correlations to infer adaptive filters for each joint in each frame. A single
model can be trained to denoise a heterogeneous mix of action types, under
substantial amounts of noise. A signal that has both noise and gaps is
preprocessed with a second bidirectional network that synthesizes missing
frames from surrounding context. The approach handles a wide variety of noise
types and long gaps, does not rely on knowledge of the noise distribution, and
operates in a streaming setting. We validate our approach through extensive
evaluations on noise both in joint angles and in joint positions, and show that
it improves upon various alternatives.

本文提出了一种利用时域相关性和关节相关性清理带噪声和不完整的运动捕捉数据的深度双向循环框架，并通过第二个双向网络对带有噪声和空缺的信号进行预处理，从周围上下文中合成缺失的帧。该方法不依赖于噪声分布的知识，处理多种类型的噪声和长空缺，并在各种评估中得到了验证。

清理动作捕捉数据的深度循环框架

A Deep Recurrent Framework for Cleaning Motion Capture Data

Today, the optimal performance of existing noise-suppression algorithms, both
data-driven and those based on classic statistical methods, is range bound to
specific levels of instantaneous input signal-to-noise ratios. In this paper,
we present a new approach to improve the adaptivity of such algorithms enabling
them to perform robustly across a wide range of input signal and noise types.
Our methodology is based on the dynamic control of algorithmic parameters via
reinforcement learning. Specifically, we model the noise-suppression module as
a black box, requiring no knowledge of the algorithmic mechanics except a
simple feedback from the output. We utilize this feedback as the reward signal
for a reinforcement-learning agent that learns a policy to adapt the
algorithmic parameters for every incoming audio frame (16 ms of data). Our
preliminary results show that such a control mechanism can substantially
increase the overall performance of the underlying noise-suppression algorithm;
42% and 16% improvements in output SNR and MSE, respectively, when compared to
no adaptivity.

本文通过强化学习控制噪声抑制算法参数，从而提升算法的鲁棒性和整体性能，实验结果显示相较于无适应性的算法有着显著的 42% 和 16% 的输出 SNR 和 MSE 改进。