Related papers: Information Inference Diagrams: Complementing Privacy and Security Analyses Beyond Data Flows

Related papers

Multiparty Session Types for GDPR Purpose Compliance [0.0]
We introduce a formal, purpose-aware framework for distributed systems.<n>Within our framework, system implementations are specified using a calculus that captures the semantics of distributed interactions and features private data as a first-class entity.
arXiv Detail & Related papers (2026-07-22T14:15:41Z)
Discrete Diffusion Models: A Unified Framework from Tokenization to Generation [66.16696467962375]
This work introduces a unified conceptual framework that views discrete diffusion models through the construction of the underlying discrete state space.<n>Within this framework, existing formulations, including transition-matrix, masking/absorbing-state, and score/ratio-based approaches, emerge as different instantiations of a common design space.
arXiv Detail & Related papers (2026-07-15T04:23:22Z)
AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding [55.535374902204296]
AffordanceVLA is a unified framework that introduces structured affordance forecasting as a task-oriented intermediate representation.<n>We show that AffordanceVLA achieves strong performance across diverse manipulation scenarios.
arXiv Detail & Related papers (2026-06-04T13:28:51Z)
Auditing Language Model Unlearning via Information Decomposition [68.48660428111593]
We introduce an interpretable, information-theoretic framework for auditing unlearning using Partial Information Decomposition (PID)<n>By comparing model representations before and after unlearning, we decompose the mutual information with the forgotten data into distinct components, formalizing the notions of unlearned and residual knowledge.<n>Our work introduces a principled, representation-level audit for unlearning, offering theoretical insight and actionable tools for safer deployment of language models.
arXiv Detail & Related papers (2026-01-21T15:51:19Z)
Discrete Diffusion in Large Language and Multimodal Models: A Survey [61.86669998363359]
We provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs)<n>Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy.<n>We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, list commonly-used modeling methods, and categorize representative models.
arXiv Detail & Related papers (2025-06-16T17:59:08Z)
FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models [12.683937179695151]
Bidirectional language models have better context understanding and perform better than unidirectional models on natural language understanding tasks.<n>We propose FlowNIB, a dynamic and scalable method for estimating mutual information during training.<n>We show that bidirectional models retain more mutual information and exhibit higher effective dimensionality than unidirectional models.
arXiv Detail & Related papers (2025-06-01T06:56:45Z)
An Algebraic Approach to Asymmetric Delegation and Polymorphic Label Inference (Technical Report) [3.183855005494611]
Language-based information flow control (IFC) enables reasoning about and enforcing security policies in decentralized applications.<n>It can be difficult to use IFC labels to model certain security assumptions, such as semi-honest agents.<n>We propose a semantic framework that allows formalizing asymmetric delegation, which is partial delegation of confidentiality or integrity.
arXiv Detail & Related papers (2025-04-29T05:00:17Z)
Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery [17.23286832909591]
We propose a set of ten challenges in transmission of knowledge derived from the philosophical literature. We find users develop workarounds to address each of the challenges. For AI developers, the Epistemic Alignment Framework offers concrete guidance for supporting diverse approaches to knowledge.
arXiv Detail & Related papers (2025-04-01T21:38:12Z)
Explainability-Driven Quality Assessment for Rule-Based Systems [0.7303392100830282]
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems. It generates explanations of rule inferences and leverages human interpretation to refine rules. Its practicality is demonstrated through a use case in finance.
arXiv Detail & Related papers (2025-02-03T11:26:09Z)
GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning framework that integrates the parametric and non-parametric memories. Our method facilitates a more logical and step-wise reasoning approach akin to experts' problem-solving, rather than gold answer retrieval.
arXiv Detail & Related papers (2024-10-11T03:05:06Z)
Establishing Knowledge Preference in Language Models [80.70632813935644]
Language models are known to encode a great amount of factual knowledge through pretraining. Such knowledge might be insufficient to cater to user requests. When answering questions about ongoing events, the model should use recent news articles to update its response. When some facts are edited in the model, the updated facts should override all prior knowledge learned by the model.
arXiv Detail & Related papers (2024-07-17T23:16:11Z)
Permissible Knowledge Pooling [0.0]
This paper introduces new modal logics for knowledge pooling and sharing. It also outlines their axiomatizations and discusses a potential framework for permissible knowledge pooling.
arXiv Detail & Related papers (2024-04-04T12:51:28Z)
Categorical semiotics: Foundations for Knowledge Integration [0.0]
We tackle the challenging task of developing a comprehensive framework for defining and analyzing deep learning architectures. Our methodology employs graphical structures that resemble Ehresmann's sketches, interpreted within a universe of fuzzy sets. This approach offers a unified theory that elegantly encompasses both deterministic and non-deterministic neural network designs.
arXiv Detail & Related papers (2024-04-01T23:19:01Z)
Controlled Training Data Generation with Diffusion Models [48.123126522294015]
We present a method to control a text-to-image generative model to produce training data useful for supervised learning.<n>We develop an automated closed-loop system which involves two feedback mechanisms.
arXiv Detail & Related papers (2024-03-22T15:59:24Z)
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning [55.715623885418815]
Cross-Domain Few-Shot Learning methods require access to source domain data to train a model in the pre-training phase. Due to increasing concerns about data privacy and the desire to reduce data transmission and training costs, it is necessary to develop a CDFSL solution without accessing source data. This paper proposes an Enhanced Information Maximization with Distance-Aware Contrastive Learning method to address these challenges.
arXiv Detail & Related papers (2024-03-04T12:10:24Z)
Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs [77.54052164713394]
This paper explores the intersection of Discrete Choice Modeling (DCM) and machine learning. We investigate the consequences of embedding high-dimensional image data that shares isomorphic information with traditional tabular inputs within a DCM framework.
arXiv Detail & Related papers (2023-12-22T14:33:54Z)
Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z)
Causal Discovery with Language Models as Imperfect Experts [119.22928856942292]
We consider how expert knowledge can be used to improve the data-driven identification of causal graphs. We propose strategies for amending such expert knowledge based on consistency properties. We report a case study, on real data, where a large language model is used as an imperfect expert.
arXiv Detail & Related papers (2023-07-05T16:01:38Z)
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models [100.4659557650775]
We propose a UNified knowledge inTERface, UNTER, to provide a unified perspective to exploit both structured knowledge and unstructured knowledge. With both forms of knowledge injected, UNTER gains continuous improvements on a series of knowledge-driven NLP tasks.
arXiv Detail & Related papers (2023-05-02T17:33:28Z)
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog [12.081212540168055]
We present a modified version of the MutliWOZ based dataset prepared by SeKnow to demonstrate how current methods have significant degradation in performance. In line with recent work exploiting pre-trained language models, we fine-tune a BART based model using prompts for the tasks of querying knowledge sources. We demonstrate that our model is robust to perturbations to knowledge modality (source of information) and that it can fuse information from structured as well as unstructured knowledge to generate responses.
arXiv Detail & Related papers (2022-10-13T18:49:59Z)
Knowledge-grounded Dialog State Tracking [12.585986197627477]
We propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context. We demonstrate superior performance of our proposed method over strong baselines.
arXiv Detail & Related papers (2022-10-13T01:34:08Z)
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty [33.334475200283435]
We introduce a dimensionality reduction surrogate modeling (DRSM) approach for representation learning and uncertainty quantification.<n>The approach involves a two-stage learning process: 1) employing a variational autoencoder to learn a low-dimensional representation of the input data distribution; and 2) harnessing the chaos expansion (PCE) to map the low dimensional distribution to the output target.
arXiv Detail & Related papers (2022-02-10T14:42:51Z)
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection [3.5557219875516655]
We propose a collaborative federated learning scheme to address privacy concerns and the lack of a universal format of datasets. The proposed framework allows multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system.
arXiv Detail & Related papers (2021-11-04T12:06:34Z)
Representations of epistemic uncertainty and awareness in data-driven strategies [0.0]
We present a theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We look at inequivalent knowledge representations in terms of inferences, preference relations, and information measures. We discuss some implications of the proposed model for data-driven strategies.
arXiv Detail & Related papers (2021-10-21T21:18:21Z)
Neural Production Systems [90.75211413357577]
Visual environments are structured, consisting of distinct objects or entities. To partition images into entities, deep-learning researchers have proposed structural inductive biases. We take inspiration from cognitive science and resurrect a classic approach, which consists of a set of rule templates. This architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information.
arXiv Detail & Related papers (2021-03-02T18:53:20Z)
Combining pre-trained language models and structured knowledge [9.521634184008574]
transformer-based language models have achieved state of the art performance in various NLP benchmarks. It has proven challenging to integrate structured information, such as knowledge graphs into these models. We examine a variety of approaches to integrate structured knowledge into current language models and determine challenges, and possible opportunities to leverage both structured and unstructured information sources.
arXiv Detail & Related papers (2021-01-28T21:54:03Z)
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA [107.7091094498848]
One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pre-training and supervised training data with transformer-based models.
arXiv Detail & Related papers (2020-12-20T20:13:02Z)
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding [68.53121591998483]
In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
arXiv Detail & Related papers (2020-09-28T15:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.