Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples with a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code is available at https://github.com/serenabooth/Bayes-TrEx.
Improving Neural Additive Models with Bayesian Principles
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Helmholtz AI（亥姆霍兹人工智能研究所）；TU Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist in the real-world decision-making process of healthcare issues. We propose a deep learning model based on Bayesian Neural Networks (BNN) to predict uncertainty induced by data noise. The uncertainty is introduced to provide model predictions with an extra level of confidence. Our …
Bayesian methods have become a popular way to incorporate prior knowledge and a notion of uncertainty into machine learning models. At the same time, the complexity of modern machine learning makes it challenging to comprehend a model's reasoning process, let alone express specific prior assumptions in a rigorous manner. While primarily interested in the former issue, recent developments intransparent machine learning could also broaden the range of prior information that we can provide to complex Bayesian models. Inspired by the idea of self-explaining models, we introduce a corresponding con…
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
University of Cambridge（剑桥大学）；Bilkent University（比尔肯大学）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；The Alan Turing Institute（艾伦·图灵研究所）；UCLA（加州大学洛杉矶分校）
Bayesian inference involves the specification of a statistical model by a statistician or practitioner, with careful thought about what each parameter represents. This results in particularly interpretable models which can be used to explain relationships present in the observed data. Bayesian models are useful when an experiment has only a small number of observations and in applications where transparency of data driven decisions is important. Traditionally, parameter inference in Bayesian statistics has involved constructing bespoke MCMC (Markov chain Monte Carlo) schemes for each newly pro…
Microsoft Research（微软研究院）；Northwestern Polytechnical University（西北工业大学）
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur kernel are required to regularize the solution space. While this naturally leads to a standard MAP estimation framework, performance is compromised by unknown trade-off parameter settings, optimization heuristics, and convergence issues stemming from non-convexity and/or poor prior selections. To mitigate some of these problems, a number of authors have recently proposed substituting a variational…
Bayesian Species Distribution Models using Hierarchical Decomposition Priors
Department of Economics University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学经济系）；Department of Statistical Sciences University of Bologna（博洛尼亚大学统计科学系）；Department of Life Sciences and Systems Biology University of Turin（都灵大学生命科学与系统生物学系）
TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits
School of EECS, Peking University（电子工程系，北京大学）；School of Integrated Circuits, Peking University（集成电路学院，北京大学）；Department of Electrical and Computer Engineering, The University of Texas at Austin（电气与计算机工程系，德克萨斯大学奥斯汀分校）；Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）
pyhgf: A neural network library for predictive coding
Interacting Minds Centre, Aarhus University（阿arhus大学交互心智中心）；Department of Psychiatry, University of Oxford（牛津大学精神病学系）；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（国际先进研究科学学院）；Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna（维也纳大学心理学系认知、情感与方法部）
