Multiple Instance Learning
Multiple Instance Learning - Related Videos
13:57
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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Cited by 43 •
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This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task.
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Motor Learning in Mirror Drawing
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Colloquially, the terms learning and memory encompass a broad range of behaviors and mental systems, everything from learning to tie a shoe to mastering calculus (and a lot in between). Experimental psychologists have divided up learning mechanisms into groups that seem to have different properties, and that seem to rely on different brain systems. A major division is between declarative and non-declarative memory, roughly, the...
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Learning and Memory: The Remember-Know Task
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Our experience of memory is varied and complex. Sometimes we remember events in vivid detail, while other times we may only have a vague sense of familiarity. Memory researchers have made a distinction between memories that are recollected versus those that are familiar. A recollected item is one that is not only remembered, but carries with it details of the time at which it was learned or encoded.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
11:35
The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool
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Cited by 104 •
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Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report methodologic details on a new test, the Multiple Sclerosis Performance Test (MSPT). This new approach to the objective of quantification of MS related disability provides a computer-based platform for precise, valid measurement of MS severity.
FAQs
The model examines the instances within each bag and learns which instance-level representations or scores are most relevant to the available bag label. It then combines that evidence through aggregation to produce the final prediction. This structure allows informative observations to influence the result without requiring a separate label for every sensor reading, image patch, or material measurement.
Not every observation in a collection necessarily contributes equally to the bag outcome. Multiple Instance Learning can identify informative instances and give their representations or scores greater influence during aggregation. For engineering data, this helps preserve localized evidence, such as a distinctive inspection signal or image patch, rather than treating every measurement as equally important.
The two approaches combine information at different stages. Representation aggregation first combines the learned descriptions of instances and uses the resulting bag representation for prediction. Score aggregation instead combines instance-level prediction scores to form the bag decision. Both mechanisms connect unlabelled instance evidence to a group-level outcome, but they organize information differently.
A suitable collection has a meaningful group-level label while its individual observations are unavailable, uncertain, or costly to annotate. Examples include sensor readings collected for one system state, image patches from one inspection, or measurements associated with one material sample. The grouping provides the context needed to relate local observations to an overall engineering decision.
First, organize related observations into bags and assign the available label to each bag. Next, provide the instances to a model that learns their representations or scores. The model then aggregates the instance information and produces a bag prediction. This workflow avoids requiring detailed labels for every reading, patch, signal, or measurement.
The framework is relevant when engineering decisions depend on collections of observations rather than one isolated measurement. Supported examples include defect detection from inspection data, condition monitoring from sensor collections, and decision-making based on material measurements. Its main practical value is reducing the need for detailed instance annotations while retaining access to informative local evidence.