The number of local model-agnostic explanation techniques proposed has grown rapidly recently. One main reason is that the bar for developing new explainability techniques is low due to the lack of optimal evaluation measures. Without rigorous measures, it is hard to have concrete evidence of whether the new explanation techniques can significantly outperform their predecessors. Our study proposes a new taxonomy for evaluating local explanations: robustness, evaluation using ground truth from synthetic datasets and interpretable models, model randomization, and human-grounded evaluation. Using this proposed taxonomy, we highlight that all categories of evaluation methods, except those based on the ground truth from interpretable models, suffer from a problem we call the "blame problem." In our study, we argue that this category of evaluation measure is a more reasonable method for evaluating local model-agnostic explanations. However, we show that even this category of evaluation measures has further limitations. The evaluation of local explanations remains an open research problem.
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分類學是分類的實踐和科學。Wikipedia類別說明了一種分類法，可以通過自動方式提取Wikipedia類別的完整分類法。截至2009年，已經證明，可以使用人工構建的分類法（例如像WordNet這樣的計算詞典的分類法）來改進和重組Wikipedia類別分類法。
從廣義上講，分類法還適用于除父子層次結構以外的關系方案，例如網絡結構。然后分類法可能包括有多父母的單身孩子，例如，“汽車”可能與父母雙方一起出現“車輛”和“鋼結構”；但是對某些人而言，這僅意味著“汽車”是幾種不同分類法的一部分。分類法也可能只是將事物組織成組，或者是按字母順序排列的列表；但是在這里，術語詞匯更合適。在知識管理中的當前用法中，分類法被認為比本體論窄，因為本體論應用了各種各樣的關系類型。
在數學上，分層分類法是給定對象集的分類樹結構。該結構的頂部是適用于所有對象的單個分類，即根節點。此根下的節點是更具體的分類，適用于總分類對象集的子集。推理的進展從一般到更具體。
Large Language Models (LLMs) have proven effective at In-Context Learning (ICL), an ability that allows them to create predictors from labeled examples. Few studies have explored the interplay between ICL and specific properties of functions it attempts to approximate. In our study, we use a formal framework to explore ICL and propose a new task of approximating functions with varying number of minima. We implement a method that allows for producing functions with given inputs as minima. We find that increasing the number of minima degrades ICL performance. At the same time, our evaluation shows that ICL outperforms 2-layer Neural Network (2NN) model. Furthermore, ICL learns faster than 2NN in all settings. We validate the findings through a set of few-shot experiments across various hyperparameter configurations.
With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures. In this paper, we restrict our attention to substantiation -- one popular quality aspect indicating whether the claims in a review are sufficiently supported by evidence -- and provide a solution automatizing this evaluation process. To achieve this goal, we first formulate the problem as claim-evidence pair extraction in scientific peer reviews, and collect SubstanReview, the first annotated dataset for this task. SubstanReview consists of 550 reviews from NLP conferences annotated by domain experts. On the basis of this dataset, we train an argument mining system to automatically analyze the level of substantiation in peer reviews. We also perform data analysis on the SubstanReview dataset to obtain meaningful insights on peer reviewing quality in NLP conferences over recent years.
Simulators have irreplaceable importance for the research and development of autonomous driving. Besides saving resources, labor, and time, simulation is the only feasible way to reproduce many severe accident scenarios. Despite their widespread adoption across academia and industry, there is an absence in the evolutionary trajectory of simulators and critical discourse on their limitations. To bridge the gap in research, this paper conducts an in-depth review of simulators for autonomous driving. It delineates the three-decade development into three stages: specialized development period, gap period, and comprehensive development, from which it detects a trend of implementing comprehensive functionalities and open-source accessibility. Then it classifies the simulators by functions, identifying five categories: traffic flow simulator, vehicle dynamics simulator, scenario editor, sensory data generator, and driving strategy validator. Simulators that amalgamate diverse features are defined as comprehensive simulators. By investigating commercial and open-source simulators, this paper reveals that the critical issues faced by simulators primarily revolve around fidelity and efficiency concerns. This paper justifies that enhancing the realism of adverse weather simulation, automated map reconstruction, and interactive traffic participants will bolster credibility. Concurrently, headless simulation and multiple-speed simulation techniques will exploit the theoretic advantages. Moreover, this paper delves into potential solutions for the identified issues. It explores qualitative and quantitative evaluation metrics to assess the simulator's performance. This paper guides users to find suitable simulators efficiently and provides instructive suggestions for developers to improve simulator efficacy purposefully.
Floating point algorithms are studied for computational problems arising in Density Functional Theory (DFT), a powerful technique to determine the electronic structure of solids, e.g., metals, oxides, or semiconductors. Specifically, we seek algorithms with provable properties for the density matrix and the corresponding electron density in atomic systems described by the Kohn-Sham equations expressed in a localized basis set. The underlying problem is a Hermitian generalized eigenvalue problem of the form $HC=SCE$, where $H$ is Hermitian and $S$ is Hermitian positive-definite (HPD). Different methods are developed and combined to solve this problem. We first describe a Hermitian pseudospectral shattering method in finite precision, and use it to obtain a new gap-independent floating point algorithm to compute all eigenvalues of a Hermitian matrix within an additive error $\delta$ in $O(T_{MM}(n)\log^2(\tfrac{n}{\delta}))$. Here $T_{MM}(n) = O(n^{\omega+\eta})$, for any $\eta>0$, and $\omega\leq 2.371552$ is the matrix multiplication exponent.To the best of our knowledge, this is the first algorithm to achieve nearly $O(n^\omega)$ bit complexity for all Hermitian eigenvalues. As by-products, we also demonstrate additive error approximations for all singular values of rectangular matrices, and, for full-rank matrices, relative error approximations for all eigenvalues, all singular values, the spectral norm, and the condition number. We finally provide a novel analysis of a logarithmically-stable Cholesky factorization algorithm, and show that it can be used to accurately transform the HPD generalized eigenproblem to a Hermitian eigenproblem in $O(T_{MM}(n))$. All these tools are combined to obtain the first provably accurate floating point algorithms with nearly $O(T_{MM}(n))$ bit complexity for the density matrix and the electron density of atomic systems.
Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e.g., purchasing and clicking). Humans perceive the world by processing the modality signals (e.g., audio, text and image), which inspired researchers to build a recommender system that can understand and interpret data from different modalities. Those models could capture the hidden relations between different modalities and possibly recover the complementary information which can not be captured by a uni-modal approach and implicit interactions. The goal of this survey is to provide a comprehensive review of the recent research efforts on the multimodal recommendation. Specifically, it shows a clear pipeline with commonly used techniques in each step and classifies the models by the methods used. Additionally, a code framework has been designed that helps researchers new in this area to understand the principles and techniques, and easily runs the SOTA models. Our framework is located at: //github.com/enoche/MMRec
Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.
When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at \url{//github.com/fahadshamshad/awesome-transformers-in-medical-imaging}.
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
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