Individuals, despite having varied life experiences and learning processes,
can communicate effectively through languages. This study aims to explore the
efficiency of language as a communication medium. We put forth two specific
hypotheses: First, discrete messages are more effective than continuous ones
when agents have diverse personal experiences. Second, communications using
multiple discrete tokens are more advantageous than those using a single token.
To valdate these hypotheses, we designed multi-agent machine learning
experiments to assess communication efficiency using various information
transmission methods between speakers and listeners. Our empirical findings
indicate that, in scenarios where agents are exposed to different data,
communicating through sentences composed of discrete tokens offers the best
inter-agent communication efficiency. The limitations of our finding include
lack of systematic advantages over other more sophisticated encoder-decoder
model such as variational autoencoder and lack of evluation on non-image
dataset, which we will leave for future studies.

本研究旨在探索语言作为一种沟通媒介的效率，发现使用由离散符号组成的句子在不同数据情境下可以提供最佳的智能体间沟通效率。

孤立智能体之间离散消息的通信效率提升

Discrete Messages Improve Communication Efficiency among Isolated  Intelligent Agents

Vision Transformer (ViT) is emerging as the state-of-the-art architecture for
image recognition. While recent studies suggest that ViTs are more robust than
their convolutional counterparts, our experiments find that ViTs trained on
ImageNet are overly reliant on local textures and fail to make adequate use of
shape information. ViTs thus have difficulties generalizing to
out-of-distribution, real-world data. To address this deficiency, we present a
simple and effective architecture modification to ViT's input layer by adding
discrete tokens produced by a vector-quantized encoder. Different from the
standard continuous pixel tokens, discrete tokens are invariant under small
perturbations and contain less information individually, which promote ViTs to
learn global information that is invariant. Experimental results demonstrate
that adding discrete representation on four architecture variants strengthens
ViT robustness by up to 12% across seven ImageNet robustness benchmarks while
maintaining the performance on ImageNet.

本文介绍了如何通过在 ViT 的输入层添加离散 token 的方法来提高其对全局信息的学习和对真实世界数据的泛化能力，并通过实验证明对于七个 ImageNet 鲁棒性基准测试，该改进方法能够让 ViT 的鲁棒性提高最多 12%，同时不影响在 ImageNet 上的性能表现。

离散表示增强视觉 Transformer 的鲁棒性

Discrete Representations Strengthen Vision Transformer Robustness

We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because the number of target classes in each step of the output depends on the
length of the input, which is variable. Problems such as sorting variable sized
sequences, and various combinatorial optimization problems belong to this
class. Our model solves the problem of variable size output dictionaries using
a recently proposed mechanism of neural attention. It differs from the previous
attention attempts in that, instead of using attention to blend hidden units of
an encoder to a context vector at each decoder step, it uses attention as a
pointer to select a member of the input sequence as the output. We call this
architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn
approximate solutions to three challenging geometric problems -- finding planar
convex hulls, computing Delaunay triangulations, and the planar Travelling
Salesman Problem -- using training examples alone. Ptr-Nets not only improve
over sequence-to-sequence with input attention, but also allow us to generalize
to variable size output dictionaries. We show that the learnt models generalize
beyond the maximum lengths they were trained on. We hope our results on these
tasks will encourage a broader exploration of neural learning for discrete
problems.

介绍一种新的神经网络结构用于学习输出序列的条件概率，能够解决存在变量数量目标类的问题，其中应用到了神经注意力机制，被称作指针网络，能够用于学习一些具有挑战性的几何问题。