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Density Matrices for Metaphor Understanding
23 Aug

Density Matrices for Metaphor Understanding

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arXiv:2408.11846v1 Announce Type: new Abstract: In physics, density matrices are used to represent mixed states, i.e. probabilistic mixtures of pure states. This concept has previously been used to model lexical ambiguity. In this paper, we consider metaphor as a type of lexical ambiguity, and examine whether metaphorical meaning can be effectively modelled using mixtures of word senses. We find that modelling metaphor is significantly more difficult than other kinds of lexical ambiguity, but that our best-performing density matrix method outperforms simple baselines as well as some neural language models. Source link lol
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Explainable Anomaly Detection: Counterfactual driven What-If Analysis
23 Aug

Explainable Anomaly Detection: Counterfactual driven What-If Analysis

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arXiv:2408.11935v1 Announce Type: new Abstract: There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring. This raises two fundamental questions: what is causing the fault and how can we fix it? Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy". The suggestions are not always actionable which may raise the…
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Statistical Challenges with Dataset Construction: Why You Will Never Have Enough Images
23 Aug

Statistical Challenges with Dataset Construction: Why You Will Never Have Enough Images

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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LLaMA based Punctuation Restoration With Forward Pass Only Decoding
23 Aug

LLaMA based Punctuation Restoration With Forward Pass Only Decoding

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Healthcare Technology Trends in 2024
23 Aug

Healthcare Technology Trends in 2024

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Nanotech has unlocked new methods to accelerate patient recovery through nanomedicine. Meanwhile, 3D printing has empowered small manufacturer to scale their clinical equipment production. Many novel tech strategies have helped improve how healthcare businesses satisfy key stakeholders and comply with clinical data protection laws. This post will summarize the top healthcare technology trends in 2024.  1| Product Performance Scenario Analyses  For a pharmaceutical enterprise planning a new drug, simulating stakeholder reception and competitively challenging scenarios is helpful. Identical use cases also enable vendors to deliver economical and ergonomic clinical equipment. These simulations help inspect product utility, potential demand, and return…
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Neural Symbolic Logical Rule Learner for Interpretable Learning
23 Aug

Neural Symbolic Logical Rule Learner for Interpretable Learning

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arXiv:2408.11918v1 Announce Type: new Abstract: Rule-based neural networks stand out for enabling interpretable classification by learning logical rules for both prediction and interpretation. However, existing models often lack flexibility due to the fixed model structure. Addressing this, we introduce the Normal Form Rule Learner (NFRL) algorithm, leveraging a selective discrete neural network, that treat weight parameters as hard selectors, to learn rules in both Conjunctive Normal Form (CNF) and Disjunctive Normal Form (DNF) for enhanced accuracy and interpretability. Instead of adopting a deep, complex structure, the NFRL incorporates two specialized Normal Form Layers (NFLs) with adaptable AND/OR neurons, a Negation…
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An Interpretable Deep Learning Approach for Morphological Script Type Analysis
23 Aug

An Interpretable Deep Learning Approach for Morphological Script Type Analysis

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[Submitted on 20 Aug 2024] View a PDF of the paper titled An Interpretable Deep Learning Approach for Morphological Script Type Analysis, by Malamatenia Vlachou-Efstathiou and 3 other authors View PDF Abstract:Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation…
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Editable Fairness: Fine-Grained Bias Mitigation in Language Models
23 Aug

Editable Fairness: Fine-Grained Bias Mitigation in Language Models

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arXiv:2408.11843v1 Announce Type: new Abstract: Generating fair and accurate predictions plays a pivotal role in deploying large language models (LLMs) in the real world. However, existing debiasing methods inevitably generate unfair or incorrect predictions as they are designed and evaluated to achieve parity across different social groups but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. In this paper, we first establish a new bias mitigation benchmark, BiaScope, which systematically assesses performance by leveraging newly constructed datasets and metrics on knowledge retention and generalization. Then, we propose a novel debiasing approach, Fairness Stamp…
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Unraveling Text Generation in LLMs: A Stochastic Differential Equation Approach
23 Aug

Unraveling Text Generation in LLMs: A Stochastic Differential Equation Approach

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arXiv:2408.11863v1 Announce Type: new Abstract: This paper explores the application of Stochastic Differential Equations (SDE) to interpret the text generation process of Large Language Models (LLMs) such as GPT-4. Text generation in LLMs is modeled as a stochastic process where each step depends on previously generated content and model parameters, sampling the next word from a vocabulary distribution. We represent this generation process using SDE to capture both deterministic trends and stochastic perturbations. The drift term describes the deterministic trends in the generation process, while the diffusion term captures the stochastic variations. We fit these functions using neural networks and…
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