In belief revision, agents typically modify their beliefs when they receive
some new piece of information that is in conflict with them. The guiding
principle behind most belief revision frameworks is that of minimalism, which
advocates minimal changes to existing beliefs. However, minimalism may not
necessarily capture the nuanced ways in which human agents reevaluate and
modify their beliefs. In contrast, the explanatory hypothesis indicates that
people are inherently driven to seek explanations for inconsistencies, thereby
striving for explanatory coherence rather than minimal changes when revising
beliefs. Our contribution in this paper is two-fold. Motivated by the
explanatory hypothesis, we first present a novel, yet simple belief revision
operator that, given a belief base and an explanation for an explanandum, it
revises the belief bases in a manner that preserves the explanandum and is not
necessarily minimal. We call this operator explanation-based belief revision.
Second, we conduct two human-subject studies to empirically validate our
approach and investigate belief revision behavior in real-world scenarios. Our
findings support the explanatory hypothesis and provide insights into the
strategies people employ when resolving inconsistencies.

通过解释假设，本研究提出了一种基于解释的信念修订操作符，以试图更好地描述人们在解决信念不一致性时所采用的策略，并进行了两项人类参与研究以验证该方法。

基于解释的信念修正：从极简主义向解释理解进发

Explanation-based Belief Revision: Moving Beyond Minimalism to  Explanatory Understanding

In this paper we compare some old formulations of Minimalism, in particular
Stabler's computational minimalism, and Chomsky's new formulation of Merge and
Minimalism, from the point of view of their mathematical description in terms
of Hopf algebras. We show that the newer formulation has a clear advantage
purely in terms of the underlying mathematical structure. More precisely, in
the case of Stabler's computational minimalism, External Merge can be described
in terms of a partially defined operated algebra with binary operation, while
Internal Merge determines a system of right-ideal coideals of the Loday-Ronco
Hopf algebra and corresponding right-module coalgebra quotients. This
mathematical structure shows that Internal and External Merge have
significantly different roles in the old formulations of Minimalism, and they
are more difficult to reconcile as facets of a single algebraic operation, as
desirable linguistically. On the other hand, we show that the newer formulation
of Minimalism naturally carries a Hopf algebra structure where Internal and
External Merge directly arise from the same operation. We also compare, at the
level of algebraic properties, the externalization model of the new Minimalism
with proposals for assignments of planar embeddings based on heads of trees.

研究了 Minimalism 和 Merge 的新及旧形式的数学描述，并比较了它们在 Hopf 代数结构方面的不同表现。结果表明，新的 Minimalism 公式在代数结构方面具有明显优势，并可以直接从相同操作中产生内部和外部合并。

新旧极简主义：一个霍普夫代数比较

Old and New Minimalism: a Hopf algebra comparison

At the heart of foundation models is the philosophy of "more is different",
exemplified by the astonishing success in computer vision and natural language
processing. However, the challenges of optimization and inherent complexity of
transformer models call for a paradigm shift towards simplicity. In this study,
we introduce VanillaNet, a neural network architecture that embraces elegance
in design. By avoiding high depth, shortcuts, and intricate operations like
self-attention, VanillaNet is refreshingly concise yet remarkably powerful.
Each layer is carefully crafted to be compact and straightforward, with
nonlinear activation functions pruned after training to restore the original
architecture. VanillaNet overcomes the challenges of inherent complexity,
making it ideal for resource-constrained environments. Its easy-to-understand
and highly simplified architecture opens new possibilities for efficient
deployment. Extensive experimentation demonstrates that VanillaNet delivers
performance on par with renowned deep neural networks and vision transformers,
showcasing the power of minimalism in deep learning. This visionary journey of
VanillaNet has significant potential to redefine the landscape and challenge
the status quo of foundation model, setting a new path for elegant and
effective model design. Pre-trained models and codes are available at
this https URL and
this https URL

本研究介绍了 VanillaNet，一种神经网络架构，其简洁而强大的特点使其适用于资源受限环境，并展示了小型化在深度学习中的优势。