22 research outputs found
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
The ever increasing prevalence of publicly available structured data on the
World Wide Web enables new applications in a variety of domains. In this paper,
we provide a conceptual approach that leverages such data in order to explain
the input-output behavior of trained artificial neural networks. We apply
existing Semantic Web technologies in order to provide an experimental proof of
concept
Explaining Deep Learning Hidden Neuron Activations using Concept Induction
One of the current key challenges in Explainable AI is in correctly
interpreting activations of hidden neurons. It seems evident that accurate
interpretations thereof would provide insights into the question what a deep
learning system has internally \emph{detected} as relevant on the input, thus
lifting some of the black box character of deep learning systems.
The state of the art on this front indicates that hidden node activations
appear to be interpretable in a way that makes sense to humans, at least in
some cases. Yet, systematic automated methods that would be able to first
hypothesize an interpretation of hidden neuron activations, and then verify it,
are mostly missing.
In this paper, we provide such a method and demonstrate that it provides
meaningful interpretations. It is based on using large-scale background
knowledge -- a class hierarchy of approx. 2 million classes curated from the
Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach
called \emph{concept induction} based on description logics that was originally
developed for applications in the Semantic Web field.
Our results show that we can automatically attach meaningful labels from the
background knowledge to individual neurons in the dense layer of a
Convolutional Neural Network through a hypothesis and verification process.Comment: Submitted to IJCAI-2
Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers
The launch of ChatGPT has garnered global attention, marking a significant
milestone in the field of Generative Artificial Intelligence. While Generative
AI has been in effect for the past decade, the introduction of ChatGPT has
ignited a new wave of research and innovation in the AI domain. This surge in
interest has led to the development and release of numerous cutting-edge tools,
such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox,
among others. These tools exhibit remarkable capabilities, encompassing tasks
ranging from text generation and music composition, image creation, video
production, code generation, and even scientific work. They are built upon
various state-of-the-art models, including Stable Diffusion, transformer models
like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial
networks. This advancement in Generative AI presents a wealth of exciting
opportunities and, simultaneously, unprecedented challenges. Throughout this
paper, we have explored these state-of-the-art models, the diverse array of
tasks they can accomplish, the challenges they pose, and the promising future
of Generative Artificial Intelligence
Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment
A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We propose a neural-symbolic system to address this limitation in this paper. It is designed as a differentiable end-to-end deep memory network that learns over abstract, generic symbols to discover entailment patterns common to any reasoning task. A key component of the system is a simple but highly effective normalization process for continuous representation learning of KG entities within memory networks. Our results show how the model, trained over a set of KGs, can effectively entail facts from KGs excluded from the training, even when the vocabulary or the domain of test KGs is completely different from the training KGs