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

Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
Diffusion01:21

Diffusion

Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the concentration...
Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

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

Sort by
Same author

Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction.

IEEE transactions on information theory·2026
Same author

Hyperphantasia: A Benchmark for Evaluating the Mental Visualization Capabilities of Multimodal LLMs.

Advances in neural information processing systems·2026
Same authorSame journal

The Rich and the Simple: On the Implicit Bias of Adam and SGD.

Advances in neural information processing systems·2026
Same author

MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI.

ArXiv·2026
Same author

ProPicker: Promptable segmentation for particle picking in cryogenic electron tomography.

Journal of structural biology·2026
Same author

MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models.

... International Conference on Learning Representations·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same journal

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Advances in neural information processing systems·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Emergence and Evolution of Interpretable Concepts in Diffusion Models.

Berk Tinaz1, Zalan Fabian1, Mahdi Soltanolkotabi1

  • 1Dept. of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.

Advances in Neural Information Processing Systems
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

Sparse Autoencoders (SAEs) reveal interpretable concepts within diffusion models. Early activation patterns predict final image composition, enabling control over image generation stages.

More Related Videos

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
06:34

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

Published on: September 2, 2016

Related Experiment Videos

Last Updated: Jun 4, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
06:34

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

Published on: September 2, 2016

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Diffusion models excel at text-to-image generation but remain black boxes.
  • Mechanistic interpretability, successful in LLMs, is underexplored in diffusion models.

Purpose of the Study:

  • To apply Sparse Autoencoders (SAEs) for understanding diffusion model internals.
  • To investigate the interpretability of diffusion model activations and control generation.

Main Methods:

  • Leveraged the SAE framework to probe a text-to-image diffusion model.
  • Analyzed spatial distributions of activated concepts in model activations.
  • Designed intervention techniques to manipulate image composition and style.

Main Results:

  • Discovered human-interpretable concepts within diffusion model activations.
  • Showed final scene composition can be predicted early in the diffusion process.
  • Demonstrated effective control over composition in early stages, style in middle stages, and texture in final stages.

Conclusions:

  • SAEs provide valuable insights into diffusion model mechanisms.
  • Image composition is established early, while style and texture are refined later.
  • This work opens avenues for interpretable and controllable diffusion-based image generation.