Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Long-Term Memory01:18

Long-Term Memory

305
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
305
Understanding Memory01:19

Understanding Memory

687
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
687
Associative Learning01:27

Associative Learning

659
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
659
Implicit Memories01:24

Implicit Memories

221
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
221
Observational Learning01:12

Observational Learning

374
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
374
Retrieval01:12

Retrieval

209
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
209

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Continuous-Time State-Space Models for Marked Event Sequences.

Advances in neural information processing systems·2026
Same authorSame journal

A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2026
Same author

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2025
Same author

A perspective for adapting generalist AI to specialized medical AI applications and their challenges.

NPJ digital medicine·2025
Same author

MediSim: Multi-granular simulation for enriching longitudinal, multi-modal electronic health records.

Patterns (New York, N.Y.)·2025
Same author

Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources.

Proceedings of the conference. Association for Computational Linguistics. Meeting·2025

Related Experiment Video

Updated: Oct 12, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
07:01

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

4.9K

Retaining Privileged Information for Multi-Task Learning.

Fengyi Tang1, Cao Xiao2, Fei Wang3

  • 1Michigan State University, East Lansing, MI, USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|November 19, 2021
PubMed
Summary

This study introduces a new method for Learning Using Privileged Information (LUPI) in multi-task learning. It improves machine learning efficiency by transferring knowledge from privileged information to related tasks.

Keywords:
Electronic Health RecordsMulti-Task LearningPrivileged Information

More Related Videos

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.0K

Related Experiment Videos

Last Updated: Oct 12, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
07:01

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

4.9K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.0K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Knowledge transfer is crucial for machine learning generalization, especially with limited data.
  • Learning Using Privileged Information (LUPI) uses a Teacher-Student model for knowledge transfer.
  • LUPI leverages privileged information (PI) available only during training to enhance student model learning.

Purpose of the Study:

  • To develop a LUPI formulation for multi-task learning settings.
  • To enable the retention and utilization of privileged information across related tasks.
  • To improve sample efficiency and generalization performance in machine learning.

Main Methods:

  • Proposed a novel feature matching algorithm to project data into a joint latent space.
  • Integrated privileged information into a multi-task learning framework.
  • Analyzed the sample complexity of the proposed LUPI method.

Main Results:

  • The joint latent space effectively retains useful knowledge from privileged information.
  • The proposed method significantly improves sample efficiency for related learning tasks.
  • The LUPI approach demonstrates potential for greater sample efficiency compared to brute force methods.

Conclusions:

  • The novel LUPI formulation successfully integrates privileged information in multi-task learning.
  • Feature matching into a joint latent space is effective for knowledge transfer.
  • This approach enhances machine learning model performance and efficiency, particularly in data-scarce scenarios.