Search Results for « Non-parametric framework »

Parametric update methods for Large Language Models (LLMs) in continual learning often face challenges such as catastrophic forgetting and the stability-plasticity dilemma. In this work, we characterize Non-Parametric Meta Continual Learning (NPMCL) as a structured approach that enables knowledge updates without subsequent retraining after one‐time meta‐training. This framework models adaptation as a Knowledge Compression-Decompression process, formalized through four core meta-abilities: (1) Query Generation for identifying information gaps; (2) Structural Matching for precise referential and temporal alignment; (3) Distillative Compression for extracting logical invariants from raw data; and (4) Constrained Reasoning for memory-guided reasoning and prior suppression. We propose that these meta-abilities constitute a domain-agnostic cognitive pipeline, potentially allowing LLMs to adapt to dynamically changing environments by leveraging dynamic external memory. This work aims to formalize the mechanistic underpinnings of such meta-cognitive protocols. The proposed framework is informed by preliminary empirical observations from logic-aligned memory architectures (e.g., CoG-MeM), with Constrained Reasoning further explored in subsequent work (e.g., Knowledge-Constrained Reasoner). In this paper, we systematize the NPMCL paradigm, explore its potential for enabling continual learning by decoupling knowledge from knowledge-utilization capabilities at the LLM level, and incorporate a small-scale evaluation with knowledge data organized in different logical chain formats to provide an exploratory validation of the framework.

Graph structure learning (GSL) aims to assist graph neural networks to yield effective node embeddings for downstream tasks, especially in scenarios with the absence of structures or the existence of unreliable edges. Most GSL models are built on the i.i.d. assumption across training and testing data. However, this assumption can be violated where test data contains out-of-distribution samples. Consequently, those models are limited in generalization, which will lead to a poor structure. On the other hand, while they have made great progress, additional optimized parameters are required due to their implementation with parametric models. To tackle the above problems, we propose a novel Generalizable and Non-parametric Structure learning framework named GNS, which can be easily and effectively applied to various tasks. GNS neither relies on i.i.d. assumption nor even involves any parameters being optimized, instead to find an appropriate similarity between nodes and an associated threshold to establish desirable structures. Specifically, we first incorporate the candidate neighbor distributions for nodes to refine the similarity. Then, we introduce an adaptive threshold discovery method inspired by Fisher's criterion to determine final structures. Extensive experiments demonstrate that GNS excels not only in OOD scenarios but also in the general classification and regression prediction tasks.

Hospital quality assessment is key subject in hospital management. Patient satisfaction called criterion F is an important quality measure. It is necessary to use F as control factor to improve gradually the hospital management process. Because F is fuzzy, I propose a so-called non-parametric quality control (NQC) method that is combination of non-parametric test and quality control chart in order to estimate F. If F becomes a vector composed of many sub-criteria then, NQC method is extended as multivariate NQC (MNQC) method used for evaluating many concerned opinions of patients. MNQC replaces the distance between observation and null hypothesis by their Pearson coefficient. This research is a proposal because I do not make experiments on NQC and MNQC yet.

This paper focuses on evaluating airline service quality from the perspective of passengers view. Until now a lot of researches has performed in airline service quality evaluation in the world but a little research has been conducted in Iran, yet. In this research a framework for measuring airline service quality in Iran is proposed. After reviewing airline service quality criteria, SSQAI model was selected because of its comprehensiveness in covering airline service quality dimensions. SSQAI questionnaire items were redesigned to adopt with Iranian airlines requirements and environmental circumstances in the Iran's economic and cultural context. This study includes fuzzy decision-making theory, considering the possible fuzzy subjective judgment of the evaluators during airline service quality evaluation. Fuzzy TOPSIS have been applied for ranking airlines service quality performances. Three major Iranian airlines which have the most passenger transfer volumes in domestic and foreign flights, were chosen for evaluation in this research. Results demonstrated Mahan airline has got the best service quality performance rank in gaining passengers' satisfaction with delivery of high quality services to its passengers, among the three major Iranian airlines. IranAir and Aseman airlines placed in the second and third rank, respectively, according to passenger's evaluation.Statistical analysis have been used in analyzing passenger responses. Due to abnormality of data, Non-parametric tests were applied. To demonstrate airline ranks in every criterion separately, Friedman test was performed. Variance analysis and Tukey test were applied to study the influence of increasing in age and educational level of passengers' on degree of their satisfaction from airline's service quality. Results showed that age has not significant relation with passenger satisfaction of airlines, however increasing in educational level demonstrated a negative impact on passengers' satisfaction from airline's service quality.

Facilitated by advancements in conflict event databases, studies have moved towards predicting armed conflict and understanding its determinants subnationally. However, existing statistical models do not capture the diffusion of armed conflict, and hence do not adequately account for its dependence across both time and space, thus inducing potential biases in the estimates of covariates of interest. To address this, we introduce a regression approach that simultaneously captures both spatial and temporal dimension of the diffusion of armed conflict through non-parametric smoothing. Using fine-grained conflict data on Africa, we demonstrate the importance of accounting for this dependence, capturing conflict diffusion up to 550km in distance and 24 months in the past, and observe that diffusion decays exponentially in both space and time. We illustrate the flexibility of our fully interpretable method by studying the role of population in the transmission of conflict. We find that conflict typically breaks out in densely populated areas, and from there diffuses, specifically to lower population areas.

Facilitated by advancements in conflict event databases, studies have moved towards predicting armed conflict and understanding its determinants subnationally. However, existing statistical models do not capture the diffusion of armed conflict, and hence do not adequately account for its dependence across both time and space, thus inducing potential biases in the estimates of covariates of interest. To address this, we introduce a regression approach that simultaneously captures both spatial and temporal dimension of the diffusion of armed conflict through non-parametric smoothing. Using fine-grained conflict data on Africa, we demonstrate the importance of accounting for this dependence, capturing conflict diffusion up to 550km in distance and 24 months in the past, and observe that diffusion decays exponentially in both space and time. We illustrate the flexibility of our fully interpretable method by studying the role of population in the transmission of conflict. We find that conflict typically breaks out in densely populated areas, and from there diffuses, specifically to lower population areas.

Facilitated by advancements in conflict event databases, studies have moved towards predicting armed conflict and understanding its determinants subnationally. However, existing statistical models do not analyze nor capture the diffusion of armed conflict, and hence do not adequately account for its dependence across both time and space. To address this, we introduce a regression approach that simultaneously captures both spatial and temporal dimension of the diffusion of armed conflict through non-parametric smoothing, while all its effects and parameters remain fully interpretable. Using fine-grained conflict data on Africa, we observe that diffusion exhibits long-lasting and far-reaching dependencies that decay exponentially in both space and time, thus highlighting the importance of controlling for these effects. We illustrate the flexibility of our method for studying conflict diffusion, by investigating the role of population in the transmission of conflict. We find that conflict typically breaks out in densely populated areas, and from there diffuses, specifically to lower population areas.

Facilitated by advancements in conflict event databases, studies have moved towards predicting armed conflict and understanding its determinants subnationally. However, existing statistical models do not capture the diffusion of armed conflict, and hence do not adequately account for its dependence across both time and space, thus inducing potential biases in the estimates of covariates of interest. To address this, we introduce a regression approach that simultaneously captures both spatial and temporal dimension of the diffusion of armed conflict through non-parametric smoothing. Using fine-grained conflict data on Africa, we demonstrate the importance of accounting for this dependence, capturing conflict diffusion up to 550km in distance and 24 months in the past, and observe that diffusion decays exponentially in both space and time. We illustrate the flexibility of our fully interpretable method by studying the role of population in the transmission of conflict. We find that conflict typically breaks out in densely populated areas, and from there diffuses, specifically to lower population areas.