In recent years, with the advent of massive computational power and the availability of huge amounts of data, Deep neural networks have enabled the exploration of uncharted areas in several domains. But at times, they under-perform due to insufficient data, poor data quality, data that might not be covering the domain broadly, etc. Knowledge-based systems leverage expert knowledge for making decisions and suitably take actions. Such systems retain interpretability in the decision-making process. This paper focuses on exploring techniques to integrate expert knowledge to the Deep Neural Networks for sequence-to-sequence and time series models to improve their performance and interpretability.
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）
Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficient learning paradigms. In this paper, we propose a Motif-topology and Reward-learning improved SNN (MR-SNN) for efficient multi-sensory integration. MR-SNN contains 13 types of 3-node Motif topologies which are first extracted from independent sing…
Artificial Immune System (AIS-MACA) a novel computational intelligence technique is can be used for strengthening the automated protein prediction system with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-MACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, stra…
The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial …
Multi-label (ML) classification is an actively researched topic currently, which deals with convoluted and overlapping boundaries that arise due to several labels being active for a particular data instance. We propose a classifier capable of extracting underlying features and introducing non-linearity to the data to handle the complex decision boundaries. A novel neural network model has been developed where the input features are subjected to two transformations adapted from multi-label functional link artificial neural network and autoencoders. First, a functional expansion of the original …
Universitat Pompeu Fabra（庞培法布拉大学）；ICREA（加泰罗尼亚高等研究院）；Institute for Bioengineering of Catalonia (IBEC)（加泰罗尼亚生物工程研究所）；Barcelona Institute of Science and Technology (BIST)（巴塞罗那科学技术研究所）
In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchma…
Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic and Management Pathway: From Single-Modality Detection to Multimodal Clinical Integration
Michigan Technological University（密歇根理工大学）；International Atomic Energy Agency（国际原子能机构）；University of Chicago（芝加哥大学）；Cook County Health and Hospitals System（库克县健康与医院系统）；Rush Medical College（拉什医学院）
Longitudinal Ensemble Integration for sequential classification with multimodal data
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai（遗传学与基因组科学系，伊坎医学院-西奈山医院）；Department of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北德克萨斯大学）；Department of Radiology, Keck School of Medicine, University of Southern California（放射学系，凯克医学院，南加州大学）
Spoken Language Systems (LSV)（口语语言系统（LSV））；Saarland Informatics Campus（萨尔兰信息学园区）；Saarland University（萨尔兰大学）
Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussi…
The Swiss AI Lab, IDSIA（瑞士人工智能实验室（IDSIA））；USI（意大利语区大学（瑞士提契诺大学））；SUPSI（瑞士南部应用科学与艺术大学）；KAUST（阿卜杜拉国王科技大学）
Short-term memory in standard, general-purpose, sequence-processing recurrent neural networks (RNNs) is stored as activations of nodes or "neurons." Generalising feedforward NNs to such RNNs is mathematically straightforward and natural, and even historical: already in 1943, McCulloch and Pitts proposed this as a surrogate to "synaptic modifications" (in effect, generalising the Lenz-Ising model, the first non-sequence processing RNN architecture of the 1920s). A lesser known alternative approach to storing short-term memory in "synaptic connections" -- by parameterising and controlling the dy…
Many machine learning algorithms have been developed in recent years to enhance the performance of a model in different aspects of artificial intelligence. But the problem persists due to inadequate data and resources. Integrating knowledge in a machine learning model can help to overcome these obstacles up to a certain degree. Incorporating knowledge is a complex task though because of various forms of knowledge representation. In this paper, we will give a brief overview of these different forms of knowledge integration and their performance in certain machine learning tasks.
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficient learning paradigms. Network architectures are also considered embodying the function of the network. Here, we propose a Motif-topology improved SNN (M-SNN) for the efficient multi-sensory integration and cognitive phenomenon simulations.…
BMW Group Research, New Technologies, Innovations, Garching bei München, Germany（宝马集团研究、新技术与创新，慕尼黑加兴，德国）；Delft University of Technology, Delft, Netherlands（代尔夫特理工大学，代尔夫特，荷兰）；Technical University of Munich, Department of Informatics, Garching bei München, Germany（慕尼黑工业大学信息学系，慕尼黑加兴，德国）；LMU Munich, München, Germany（慕尼黑大学，慕尼黑，德国）
Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed to algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed …
POLI University of Pernambuco（伯南布哥大学POLI）；FITec（FITec基金会）；CIN Federal University of Pernambuco（伯南布哥联邦大学CIN）
All productive branches of society need an estimate to be able to control their expenses well. In the energy business, electric utilities use this information to control the power flow in the grid. For better energy production estimation of photovoltaic systems, it is necessary to join multiples geospatial and meteorological variables. This work proposes the creation of a satellite data integration platform, with production estimation models, base stations measurement and actual production capacity. This work presents statistical, probabilistic and artificial intelligence models that generate …
Xidian University（西安电子科技大学）；Northumbria University（诺森比亚大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration appr…
