Neural topic models can successfully find coherent and diverse topics in
textual data. However, they are limited in dealing with multimodal datasets
(e.g., images and text). This paper presents the first systematic and
comprehensive evaluation of multimodal topic modeling of documents containing
both text and images. In the process, we propose two novel topic modeling
solutions and two novel evaluation metrics. Overall, our evaluation on an
unprecedented rich and diverse collection of datasets indicates that both of
our models generate coherent and diverse topics. Nevertheless, the extent to
which one method outperforms the other depends on the metrics and dataset
combinations, which suggests further exploration of hybrid solutions in the
future. Notably, our succinct human evaluation aligns with the outcomes
determined by our proposed metrics. This alignment not only reinforces the
credibility of our metrics but also highlights the potential for their
application in guiding future multimodal topic modeling endeavors.

本文提出了首次系统全面评估多模式主题建模的方法，并提出了两个新的主题建模解决方案和两个新的评估指标。通过对数据集的评估，我们发现我们的模型都能生成连贯且多样化的主题。然而，一个方法优于另一个方法的程度取决于评估指标和数据集的组合，这表明未来需要进一步探索混合解决方案。值得注意的是，我们简明的人工评估与我们提出的评估指标结果一致，这不仅强化了评估指标的可信度，也突出了其在引导未来多模式主题建模研究中的潜力。

神经多模态主题建模：全面评估

Neural Multimodal Topic Modeling: A Comprehensive Evaluation

Increasing interest in ensuring safety of next-generation Artificial
Intelligence (AI) systems calls for novel approaches to embedding morality into
autonomous agents. Traditionally, this has been done by imposing explicit
top-down rules or hard constraints on systems, for example by filtering system
outputs through pre-defined ethical rules. Recently, instead, entirely
bottom-up methods for learning implicit preferences from human behavior have
become increasingly popular, such as those for training and fine-tuning Large
Language Models. In this paper, we provide a systematization of existing
approaches to the problem of introducing morality in machines - modeled as a
continuum, and argue that the majority of popular techniques lie at the
extremes - either being fully hard-coded, or entirely learned, where no
explicit statement of any moral principle is required. Given the relative
strengths and weaknesses of each type of methodology, we argue that more hybrid
solutions are needed to create adaptable and robust, yet more controllable and
interpretable agents.
In particular, we present three case studies of recent works which use
learning from experience (i.e., Reinforcement Learning) to explicitly provide
moral principles to learning agents - either as intrinsic rewards, moral
logical constraints or textual principles for language models. For example,
using intrinsic rewards in Social Dilemma games, we demonstrate how it is
possible to represent classical moral frameworks for agents. We also present an
overview of the existing work in this area in order to provide empirical
evidence for the potential of this hybrid approach. We then discuss strategies
for evaluating the effectiveness of moral learning agents. Finally, we present
open research questions and implications for the future of AI safety and ethics
which are emerging from this framework.

下一代人工智能系统的安全性越来越受关注，需要将道德性融入自主代理中。本文系统化地介绍了在机器中引入道德性的现有方法，并提出了需要更多混合解决方案创建适应性强、稳健可控且可解释的代理的论点。同时，通过案例研究和评估道德学习代理的有效性，探讨了未来人工智能安全和伦理面临的挑战。

通过经验与互动学习机器道德

Learning Machine Morality through Experience and Interaction

In recent years we have witnessed a renewed interest in machine learning
methodologies, especially for deep representation learning, that could overcome
basic i.i.d. assumptions and tackle non-stationary environments subject to
various distributional shifts or sample selection biases. Within this context,
several computational approaches based on architectural priors, regularizers
and replay policies have been proposed with different degrees of success
depending on the specific scenario in which they were developed and assessed.
However, designing comprehensive hybrid solutions that can flexibly and
generally be applied with tunable efficiency-effectiveness trade-offs still
seems a distant goal. In this paper, we propose "Architect, Regularize and
Replay" (ARR), an hybrid generalization of the renowned AR1 algorithm and its
variants, that can achieve state-of-the-art results in classic scenarios (e.g.
class-incremental learning) but also generalize to arbitrary data streams
generated from real-world datasets such as CIFAR-100, CORe50 and ImageNet-1000.

本文提出了一种名为 “Architect, Regularize and Replay”（ARR）的混合通用算法，其结合了架构先验、正则化器和回放策略，可在各种数据分布转移或样本选择偏差的情况下灵活适用，并在经典场景（如分类增量学习）以及来自 CIFAR-100，CORe50 和 ImageNet-1000 等真实世界数据集的任意数据流中实现了最先进的结果。