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Orthogonal Trajectories01:26

Orthogonal Trajectories

71
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
71
Interference and Diffraction02:18

Interference and Diffraction

52.6K
Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
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RNA Interference01:23

RNA Interference

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
28.2K
Interference and Decay01:16

Interference and Decay

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
486
Sound Waves: Interference00:53

Sound Waves: Interference

4.8K
Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
4.8K
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

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When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...
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Updated: Feb 12, 2026

Calibration Procedures for Orthogonal Superposition Rheology
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Calibration Procedures for Orthogonal Superposition Rheology

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Desentrañar la interferencia LoRA: Subespacios ortogonales para la fusión de modelos robustos.

Haobo Zhang1, Jiayu Zhou1

  • 1University of Michigan, Ann Arbor, USA.

Proceedings of the conference. Association for Computational Linguistics. Meeting
|February 11, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Introducimos subespacios ortogonales para la fusión de modelos robustos (OSRM) para fusionar eficazmente múltiples modelos de adaptación de rango bajo (LoRA). OSRM evita la interferencia de tareas, mejorando el rendimiento y preservando la precisión para una fusión robusta de modelos.

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Área de la Ciencia:

  • La inteligencia artificial es inteligencia artificial.
  • Aprendizaje automático Aprendizaje automático.
  • Procesamiento del lenguaje natural.

Sus antecedentes:

  • El ajuste fino de los grandes modelos de lenguaje (LM) para tareas específicas mejora el rendimiento, pero incurre en altos costos de implementación y almacenamiento.
  • La fusión de modelos tiene como objetivo combinar múltiples modelos específicos de tareas en un solo modelo multitarea sin necesidad de capacitación.
  • Las técnicas de fusión existentes luchan con los modelos afinados utilizando la adaptación de rango bajo (LoRA), lo que a menudo conduce a la degradación del rendimiento.

Objetivo del estudio:

  • Para abordar los problemas de degradación del rendimiento al fusionar modelos LoRA-ajustados finamente.
  • Proponer un método novedoso que permita una fusión robusta de modelos LoRA considerando la interacción entre los parámetros del modelo y las distribuciones de datos.
  • Mejorar la eficiencia y efectividad de la creación de modelos multi-tarea a partir de modelos específicos de tareas individuales.

Principales métodos:

  • Subespacios ortogonales propuestos para la fusión de modelos robustos (OSRM) para restringir el subespacio LoRA antes del ajuste fino.
  • Aseguró que las actualizaciones específicas de tareas no afectan negativamente a otras tareas.
  • OSRM integrado con algoritmos de fusión existentes para minimizar la interferencia entre las tareas.

Principales resultados:

  • OSRM aumenta significativamente el rendimiento de los modelos fusionados en comparación con los métodos existentes.
  • El método propuesto preserva con éxito la precisión de una sola tarea después de la fusión.
  • Los experimentos en varios conjuntos de datos y LM demostraron la efectividad y la robustez de OSRM para fusionar hiperparámetros.
  • OSRM mostró una mejor robustez para la fusión de hiperparámetros.

Conclusiones:

  • La interacción entre los datos y los parámetros es crucial para la fusión efectiva de modelos, especialmente con LoRA.
  • OSRM ofrece una solución plug-and-play para la fusión de modelos LoRA afinados, mejorando las capacidades de aprendizaje multitarea.
  • El método proporciona un enfoque práctico para reducir los costos de implementación mientras se mantiene un alto rendimiento en múltiples tareas.