Tree ensemble methods represent a popular machine learning model, known for
their effectiveness in supervised classification and regression tasks. Their
performance derives from aggregating predictions of multiple decision trees,
which are renowned for their interpretability properties. However, tree
ensemble methods do not reliably exhibit interpretable output. Our work aims to
extract an optimized list of rules from a trained tree ensemble, providing the
user with a condensed, interpretable model that retains most of the predictive
power of the full model. Our approach consists of solving a clean and neat set
partitioning problem formulated through Integer Programming. The proposed
method works with either tabular or time series data, for both classification
and regression tasks, and does not require parameter tuning under the most
common setting. Through rigorous computational experiments, we offer
statistically significant evidence that our method is competitive with other
rule extraction methods and effectively handles time series.

从训练的树集成中提取一组优化的规则，提供给用户一个简化、可解释的模型，保留了完整模型的大部分预测能力。

通过整数规划从树集合中提取可解释规则的统一方法

A Unified Approach to Extract Intepretable Rules from Tree Ensembles via  Integer Programming

Large language models have demonstrated robust performance on various
language tasks using zero-shot or few-shot learning paradigms. While being
actively researched, multimodal models that can additionally handle images as
input have yet to catch up in size and generality with language-only models. In
this work, we ask whether language-only models can be utilised for tasks that
require visual input -- but also, as we argue, often require a strong reasoning
component. Similar to some recent related work, we make visual information
accessible to the language model using separate verbalisation models.
Specifically, we investigate the performance of open-source, open-access
language models against GPT-3 on five vision-language tasks when given
textually-encoded visual information. Our results suggest that language models
are effective for solving vision-language tasks even with limited samples. This
approach also enhances the interpretability of a model's output by providing a
means of tracing the output back through the verbalised image content.

本篇文章研究了如何通过联合对话模型和语言模型使其能够有效地处理视觉信息，解决了在有限样本时视觉 - 语言任务的问题，使输出更易于解释。

语言空间中的图像：探索大语言模型在视觉和语言任务中的适用性

Images in Language Space: Exploring the Suitability of Large Language  Models for Vision & Language Tasks

Questions that require counting a variety of objects in images remain a major
challenge in visual question answering (VQA). The most common approaches to VQA
involve either classifying answers based on fixed length representations of
both the image and question or summing fractional counts estimated from each
section of the image. In contrast, we treat counting as a sequential decision
process and force our model to make discrete choices of what to count.
Specifically, the model sequentially selects from detected objects and learns
interactions between objects that influence subsequent selections. A
distinction of our approach is its intuitive and interpretable output, as
discrete counts are automatically grounded in the image. Furthermore, our
method outperforms the state of the art architecture for VQA on multiple
metrics that evaluate counting.

采用序列决策过程将计数视为挑选特定对象的离散选择，从而得到可以解释的输出。该方法在评估计数的多项度量标准上优于当前最先进的视觉问答框架。

可解释的视觉问答计数

Interpretable Counting for Visual Question Answering

We present a simple nearest-neighbor (NN) approach that synthesizes
high-frequency photorealistic images from an "incomplete" signal such as a
low-resolution image, a surface normal map, or edges. Current state-of-the-art
deep generative models designed for such conditional image synthesis lack two
important things: (1) they are unable to generate a large set of diverse
outputs, due to the mode collapse problem. (2) they are not interpretable,
making it difficult to control the synthesized output. We demonstrate that NN
approaches potentially address such limitations, but suffer in accuracy on
small datasets. We design a simple pipeline that combines the best of both
worlds: the first stage uses a convolutional neural network (CNN) to maps the
input to a (overly-smoothed) image, and the second stage uses a pixel-wise
nearest neighbor method to map the smoothed output to multiple high-quality,
high-frequency outputs in a controllable manner. We demonstrate our approach
for various input modalities, and for various domains ranging from human faces
to cats-and-dogs to shoes and handbags.

本文提出了一种简单的最近邻方法，可以从 “不完整” 的信号（如低分辨率图像、表面法线图或边缘）中合成高频逼真的图像。通过将卷积神经网络和最邻近法相结合，可以解决模式崩溃问题和输出可控性。