455,495 research outputs found
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Organizing memories for generalization in complementary learning systems
Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize safe routes to specific water sources and generalize from these memories to discover environmental features that predict new ones. These functions depend on systems consolidation mechanisms that construct neocortical memory traces from hippocampal precursors, but why systems consolidation only applies to a subset of hippocampal memories is unclear. Here we introduce a new neural network formalization of systems consolidation that reveals an overlooked tension—unregulated neocortical memory transfer can cause overfitting and harm generalization in an unpredictable world. We resolve this tension by postulating that memories only consolidate when it aids generalization. This framework accounts for partial hippocampal–cortical memory transfer and provides a normative principle for reconceptualizing numerous observations in the field. Generalization-optimized systems consolidation thus provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems, especially those based on artificial neural
networks, one of the biggest obstacles is the severe inability to retain old
knowledge as new information is encountered. This phenomenon is known as
catastrophic forgetting. In this article, we propose a new kind of
connectionist architecture, the Sequential Neural Coding Network, that is
robust to forgetting when learning from streams of data points and, unlike
networks of today, does not learn via the immensely popular back-propagation of
errors. Grounded in the neurocognitive theory of predictive processing, our
model adapts its synapses in a biologically-plausible fashion, while another,
complementary neural system rapidly learns to direct and control this
cortex-like structure by mimicking the task-executive control functionality of
the basal ganglia. In our experiments, we demonstrate that our self-organizing
system experiences significantly less forgetting as compared to standard neural
models and outperforms a wide swath of previously proposed methods even though
it is trained across task datasets in a stream-like fashion. The promising
performance of our complementary system on benchmarks, e.g., SplitMNIST, Split
Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating
mechanisms prominent in real neuronal systems, such as competition, sparse
activation patterns, and iterative input processing, a new possibility for
tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split
mnist/fmnist/not-mnist. Task selection/basal ganglia model has been
integrate
Recommended from our members
Explanation-based learning for diagnosis
Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required
Learning Capacity as an Industry Capability
Industrial learning (i.e., interorganizational creation, diffusion and management of information assets) is portrayed as an industry capability which is differentially accessed by firms depending on their absorption capacity and relative bargaining power, both of which are determined by their complementary resources. IT enhances industrial learning by improving communication capabilities and optimizing incentive systems. IT also enables individual firms\u27 appropriation and exploitation of industrial learning to the extent that it leverages complementary firm resource
- …