We review the literature on trainable, compressed embedding layers and
discuss their applicability for compressing gigantic neural recommender
systems. We also report the results we measured with our compressed embedding
layers.

本文回顾可训练的、压缩的嵌入层的文献，并讨论它们压缩庞大的神经推荐系统的适用性，同时报告我们使用压缩嵌入层测量的结果。

压缩嵌入层及其在推荐系统中的应用综述

Review of compressed embedding layers and their applications for  recommender systems

Recent research has found that the activation function (AF) selected for
adding non-linearity into the output can have a big impact on how effectively
deep learning networks perform. Developing activation functions that can adapt
simultaneously with learning is a need of time. Researchers recently started
developing activation functions that can be trained throughout the learning
process, known as trainable, or adaptive activation functions (AAF). Research
on AAF that enhance the outcomes is still in its early stages. In this paper, a
novel activation function 'ErfReLU' has been developed based on the erf
function and ReLU. This function exploits the ReLU and the error function (erf)
to its advantage. State of art activation functions like Sigmoid, ReLU, Tanh,
and their properties have been briefly explained. Adaptive activation functions
like Tanhsoft1, Tanhsoft2, Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and
Serf have also been described. Lastly, performance analysis of 9 trainable
activation functions along with the proposed one namely Tanhsoft1, Tanhsoft2,
Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and Serf has been shown by
applying these activation functions in MobileNet, VGG16, and ResNet models on
CIFAR-10, MNIST, and FMNIST benchmark datasets.

本文介绍了一种基于 Erf 函数和 ReLU 的新型激活函数 'ErfReLU'，并比较了其与其他 9 种可训练激活函数在 CIFAR-10，MNIST 和 FMNIST 基准数据集上应用于 MobileNet，VGG16，ResNet 模型的性能分析。

ErfReLU: 深度神经网络自适应激活函数

ErfReLU: Adaptive Activation Function for Deep Neural Network

We propose a novel image retouching method by modeling the retouching process
as performing a sequence of newly introduced trainable neural color operators.
The neural color operator mimics the behavior of traditional color operators
and learns pixelwise color transformation while its strength is controlled by a
scalar. To reflect the homomorphism property of color operators, we employ
equivariant mapping and adopt an encoder-decoder structure which maps the
non-linear color transformation to a much simpler transformation (i.e.,
translation) in a high dimensional space. The scalar strength of each neural
color operator is predicted using CNN based strength predictors by analyzing
global image statistics. Overall, our method is rather lightweight and offers
flexible controls. Experiments and user studies on public datasets show that
our method consistently achieves the best results compared with SOTA methods in
both quantitative measures and visual qualities. The code and pretrained models
are provided at this https URL

本研究提出了一种使用可训练的神经彩色算子来模拟传统彩色算子的影像修复方法，使用编码器 - 解码器结构来将非线性的颜色变换映射到高维空间的简单变换，通过全局图像统计分析来使用 CNN 强度预测器预测每个神经彩色算子的标量强度，经实验和用户研究证明，该方法在定量测量和可视化质量方面都能显著提高修复效果。

序列图像修饰的神经色彩算子

Neural Color Operators for Sequential Image Retouching

In this paper we present the design and evaluation of an end-to-end
trainable, deep neural network with a visual attention mechanism for
memorability estimation in still images. We analyze the suitability of transfer
learning of deep models from image classification to the memorability task.
Further on we study the impact of the attention mechanism on the memorability
estimation and evaluate our network on the SUN Memorability and the LaMem
datasets. Our network outperforms the existing state of the art models on both
datasets in terms of the Spearman's rank correlation as well as the mean
squared error, closely matching human consistency.

本文介绍了采用可视化注意力机制的全可训练、深度神经网络的设计和评估，用于记忆性估计。研究了从图像分类到记忆任务的深层模型的迁移学习的适用性和关注机制对记忆性估计的影响，并在 SUN 记忆力和 LaMem 数据集上评估了网络，在 Spearman 等级相关性和平均平均误差方面优于现有技术水平模型，与人类的一致性接近。