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Related Concept Videos

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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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.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Related Experiment Video

Updated: Apr 28, 2026

Cross-Modal Multivariate Pattern Analysis
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ViSymRe: Vision multimodal symbolic regression.

Da Li1, Junping Yin2, Jin Xu3

  • 1Academy for Advanced Interdisciplinary Studies, Northeast Normal University, Changchun, 130024, Jilin, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

Vision Symbolic Regression (ViSymRe) enhances artificial intelligence by integrating visual data, improving equation discovery from complex datasets. This approach overcomes high-dimensional challenges for efficient and accurate symbolic regression.

Keywords:
Biased cross-attentionMulti-view random slicingMultimodal learningScientific discoveryVision symbolic regression

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Symbolic regression (SR) aims to extract interpretable equations from data.
  • Transformer models have advanced SR but struggle with modal heterogeneity.
  • Existing methods face challenges in high-dimensional data and dataset-only deployment.

Purpose of the Study:

  • Introduce ViSymRe, a Vision Symbolic Regression framework using visual modality to boost Transformer-based SR.
  • Address untrainability in high-dimensional visual SR scenarios.
  • Enable dataset-only deployment for ViSymRe.

Main Methods:

  • Propose Multi-View Random Slicing (MVRS) to project multivariate equations into 2D, mitigating high-dimensional visualization issues.
  • Design a dual-visual pipeline with a Visual Decoder for dataset-driven feature reconstruction.
  • Implement a Biased Cross-Attention module to fuse visual features and suppress noise.

Main Results:

  • Visual modality positively impacts model convergence and enhances SR metrics.
  • ViSymRe demonstrates competitive performance against baselines on benchmarks.
  • The framework shows particular strength in low-complexity and rapid-inference scenarios.

Conclusions:

  • ViSymRe effectively leverages visual information to improve symbolic regression.
  • MVRS and the dual-visual pipeline enable efficient training and dataset-only deployment.
  • The proposed framework offers a promising direction for advancing AI in scientific discovery.