Related papers: PiShield: A PyTorch Package for Learning with Requirements

PiShield: A PyTorch Package for Learning with Requirements

URL: http://arxiv.org/abs/2402.18285v2
Date: Tue, 14 May 2024 17:23:13 GMT
Title: PiShield: A PyTorch Package for Learning with Requirements
Authors: Mihaela Cătălina Stoian, Alex Tatomir, Thomas Lukasiewicz, Eleonora Giunchiglia,
Abstract summary: Deep learning models often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology.
Score: 49.03568411956408
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, regardless of input. Additionally, it allows for integrating requirements both at inference and/or training time, depending on the practitioners' needs. Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains. Here, we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation.

Related papers

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers [0.8499685241219366]
Language models have gained significant interest due to their general-purpose capabilities, which appear to emerge as models are scaled to increasingly larger parameter sizes. Large models impose stringent requirements on computing systems, necessitating significant memory and processing requirements for inference. This makes performing inference on mobile and edge devices challenging, often requiring invocating remotely-hosted models via network calls.
arXiv Detail & Related papers (2024-12-19T12:28:27Z)
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence [18.510632104241523]
Four key pillars of sustainable machine learning are fairness, privacy, interpretability and greenhouse gas emissions. There are inherent trade-offs between each of the pillars, making it more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning and proposes FPIG, a general AI pipeline.
arXiv Detail & Related papers (2024-07-17T09:54:19Z)
Towards an Approach to Pattern-based Domain-Specific Requirements Engineering [0.0]
We propose the Pattern-based Domain-specific Requirements Engineering Approach for the specification of functional and performance requirements. This approach emerges from an academia-industry collaboration and is our first attempt to frame an approach which allows for analyzing domain knowledge. Our contribution is two-fold: First, we present a solution to pattern-based domain-specific requirements engineering and its exemplary integration into quality assurance techniques.
arXiv Detail & Related papers (2024-04-26T11:38:55Z)
Requirements' Characteristics: How do they Impact on Project Budget in a Systems Engineering Context? [3.2872885101161318]
Controlling and assuring the quality of natural language requirements (NLRs) is challenging. We investigated with the Swedish Transportation Agency (STA) to what extent the characteristics of requirements had an influence on change requests and budget changes in the project.
arXiv Detail & Related papers (2023-10-02T17:53:54Z)
FedYolo: Augmenting Federated Learning with Pretrained Transformers [61.56476056444933]
In this work, we investigate pretrained transformers (PTF) to achieve on-device learning goals. We show that larger scale shrinks the accuracy gaps between alternative approaches and improves robustness. Finally, it enables clients to solve multiple unrelated tasks simultaneously using a single PTF.
arXiv Detail & Related papers (2023-07-10T21:08:52Z)
Machine Learning with Requirements: a Manifesto [114.97965827971132]
We argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline.
arXiv Detail & Related papers (2023-04-07T14:47:13Z)
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey [53.258091735278875]
This survey covers studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey proceeds to cover three categories of the state-of-the-art of deep model design automation techniques.
arXiv Detail & Related papers (2022-08-22T12:12:43Z)
SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z)
Conditional Generative Modeling via Learning the Latent Space [54.620761775441046]
We propose a novel framework for conditional generation in multimodal spaces. It uses latent variables to model generalizable learning patterns. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
arXiv Detail & Related papers (2020-10-07T03:11:34Z)

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