Neuro-Symbolic Learning: Principles and Applications in Ophthalmology

• URL: http://arxiv.org/abs/2208.00374v1
• Date: Sun, 31 Jul 2022 06:48:19 GMT
• Title: Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
• Authors: Muhammad Hassan, Haifei Guan, Aikaterini Melliou, Yuqi Wang, Qianhui Sun, Sen Zeng, Wen Liang, Yiwei Zhang, Ziheng Zhang, Qiuyue Hu, Yang Liu, Shunkai Shi, Lin An, Shuyue Ma, Ijaz Gul, Muhammad Akmal Rahee, Zhou You, Canyang Zhang, Vijay Kumar Pandey, Yuxing Han, Yongbing Zhang, Ming Xu, Qiming Huang, Jiefu Tan, Qi Xing, Peiwu Qin, Dongmei Yu
• Abstract summary: The neuro-symbolic learning (NeSyL) notion incorporates aspects of symbolic representation and bringing common sense into neural networks (NeSyL) NeSyL has shown promising outcomes in domains where interpretability, reasoning, and explainability are crucial, such as video and image captioning, question-answering and reasoning, health informatics, and genomics. This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly, future perspectives of this emerging field.
• Score: 20.693460748187906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed for critical applications. Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations. Thus, the neuro-symbolic learning (NeSyL) notion emerged, which incorporates aspects of symbolic representation and bringing common sense into neural networks (NeSyL). In domains where interpretability, reasoning, and explainability are crucial, such as video and image captioning, question-answering and reasoning, health informatics, and genomics, NeSyL has shown promising outcomes. This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly, future perspectives of this emerging field.

Related papers

• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Simple and Effective Transfer Learning for Neuro-Symbolic Integration [50.592338727912946]
  A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning. Most of these methods exploit a neural network to map perceptions to symbols and a logical reasoner to predict the output of the downstream task. They suffer from several issues, including slow convergence, learning difficulties with complex perception tasks, and convergence to local minima. This paper proposes a simple yet effective method to ameliorate these problems.
  arXiv  Detail & Related papers  (2024-02-21T15:51:01Z)
• NeuralFastLAS: Fast Logic-Based Learning from Raw Data [54.938128496934695]
  Symbolic rule learners generate interpretable solutions, however they require the input to be encoded symbolically. Neuro-symbolic approaches overcome this issue by mapping raw data to latent symbolic concepts using a neural network. We introduce NeuralFastLAS, a scalable and fast end-to-end approach that trains a neural network jointly with a symbolic learner.
  arXiv  Detail & Related papers  (2023-10-08T12:33:42Z)
• Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
  Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years. NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
  arXiv  Detail & Related papers  (2022-10-28T04:38:10Z)
• Survey on Applications of Neurosymbolic Artificial Intelligence [37.7665470475176]
  We introduce a taxonomy of common Neurosymbolic applications and summarize the state-of-the-art for each of those domains. We identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.
  arXiv  Detail & Related papers  (2022-09-08T18:18:41Z)
• Searching for the Essence of Adversarial Perturbations [73.96215665913797]
  We show that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's erroneous prediction. This concept of human-recognizable information allows us to explain key features related to adversarial perturbations.
  arXiv  Detail & Related papers  (2022-05-30T18:04:57Z)
• Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
  Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data. We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data. NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
  arXiv  Detail & Related papers  (2022-05-25T12:41:59Z)
• Mathematical Models of Overparameterized Neural Networks [25.329225766892126]
  We will focus on the analysis of two-layer neural networks, and explain the key mathematical models. We will then discuss challenges in understanding deep neural networks and some current research directions.
  arXiv  Detail & Related papers  (2020-12-27T17:48:31Z)
• Neurosymbolic AI: The 3rd Wave [1.14219428942199]
  Concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning. Neural-symbolic computing has been an active area of research seeking to bring together robust learning in neural networks with reasoning and explainability.
  arXiv  Detail & Related papers  (2020-12-10T18:31:38Z)
• On Interpretability of Artificial Neural Networks: A Survey [21.905647127437685]
  We systematically review recent studies in understanding the mechanism of neural networks, describe applications of interpretability especially in medicine. We discuss future directions of interpretability research, such as in relation to fuzzy logic and brain science.
  arXiv  Detail & Related papers  (2020-01-08T13:40:42Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.