Results 11 to 20 of about 1,818,152 (349)
Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability
Journal of Metaverse, 2023 Artificial Intelligence (AI) is rapidly integrating into various aspects of our daily lives, influencing decision-making processes in areas such as targeted advertising and matchmaking algorithms. As AI systems become increasingly sophisticated, ensuring
Khaleque Insia +5 more
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Abstracting Fairness: Oracles, Metrics, and Interpretability [PDF]
, 2020 It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account.
Dwork, Cynthia +3 more
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Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques
Energies, 2023 Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their high ...
Xianlin Ma +3 more
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Interpretability at Scale: Identifying Causal Mechanisms in Alpaca [PDF]
Neural Information Processing Systems, 2023 Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability methods are faithful to the causal dynamics underlying model behavior and ...
Zhengxuan Wu +3 more
semanticscholar +1 more source
T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting
Energies, 2023 Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development.
Mengkun Liang +4 more
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Axioms, 2023 In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing ...
Zhouyuan Chen, Zhichao Lian, Zhe Xu
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Inseq: An Interpretability Toolkit for Sequence Generation Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools.
Gabriele Sarti +5 more
semanticscholar +1 more source
Transformer Interpretability Beyond Attention Visualization [PDF]
Computer Vision and Pattern Recognition, 2020 Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.
Hila Chefer, Shir Gur, Lior Wolf
semanticscholar +1 more source
Network Dissection: Quantifying Interpretability of Deep Visual Representations [PDF]
Computer Vision and Pattern Recognition, 2017 We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts.
David Bau +4 more
semanticscholar +1 more source
Tracr: Compiled Transformers as a Laboratory for Interpretability [PDF]
Neural Information Processing Systems, 2023 We show how to"compile"human-readable programs into standard decoder-only transformer models. Our compiler, Tracr, generates models with known structure. This structure can be used to design experiments.
David Lindner +4 more
semanticscholar +1 more source