Evaluating Representational Similarity Measures from the Lens of Functional Correspondence

• URL: http://arxiv.org/abs/2411.14633v1
• Date: Thu, 21 Nov 2024 23:53:58 GMT
• Title: Evaluating Representational Similarity Measures from the Lens of Functional Correspondence
• Authors: Yiqing Bo, Ansh Soni, Sudhanshu Srivastava, Meenakshi Khosla,
• Abstract summary: Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data. Despite the widespread use of representational comparisons, a critical question remains: which metrics are most suitable for these comparisons?
• Score: 1.7811840395202345
• License:
• Abstract: Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data, where the comparative analysis of such data is crucial for revealing shared mechanisms and differences between these complex systems. Despite the widespread use of representational comparisons and the abundance classes of comparison methods, a critical question remains: which metrics are most suitable for these comparisons? While some studies evaluate metrics based on their ability to differentiate models of different origins or constructions (e.g., various architectures), another approach is to assess how well they distinguish models that exhibit distinct behaviors. To investigate this, we examine the degree of alignment between various representational similarity measures and behavioral outcomes, employing group statistics and a comprehensive suite of behavioral metrics for comparison. In our evaluation of eight commonly used representational similarity metrics in the visual domain -- spanning alignment-based, Canonical Correlation Analysis (CCA)-based, inner product kernel-based, and nearest-neighbor methods -- we found that metrics like linear Centered Kernel Alignment (CKA) and Procrustes distance, which emphasize the overall geometric structure or shape of representations, excelled in differentiating trained from untrained models and aligning with behavioral measures, whereas metrics such as linear predictivity, commonly used in neuroscience, demonstrated only moderate alignment with behavior. These insights are crucial for selecting metrics that emphasize behaviorally meaningful comparisons in NeuroAI research.

Related papers

• Measuring similarity between embedding spaces using induced neighborhood graphs [10.056989400384772]
  We propose a metric to evaluate the similarity between paired item representations. Our results show that accuracy in both analogy and zero-shot classification tasks correlates with the embedding similarity.
  arXiv  Detail & Related papers  (2024-11-13T15:22:33Z)
• Analyzing Generative Models by Manifold Entropic Metrics [8.477943884416023]
  We introduce a novel set of tractable information-theoretic evaluation metrics. We compare various normalizing flow architectures and $beta$-VAEs on the EMNIST dataset. The most interesting finding of our experiments is a ranking of model architectures and training procedures in terms of their inductive bias to converge to aligned and disentangled representations during training.
  arXiv  Detail & Related papers  (2024-10-25T09:35:00Z)
• Supervised Pattern Recognition Involving Skewed Feature Densities [49.48516314472825]
  The classification potential of the Euclidean distance and a dissimilarity index based on the coincidence similarity index are compared. The accuracy of classifying the intersection point between the densities of two adjacent groups is taken into account.
  arXiv  Detail & Related papers  (2024-09-02T12:45:18Z)
• Differentiable Optimization of Similarity Scores Between Models and Brains [1.5391321019692434]
  Similarity measures such as linear regression, Centered Kernel Alignment (CKA), Normalized Bures Similarity (NBS), and angular Procrustes distance are often used to quantify this similarity. Here, we introduce a novel tool to investigate what drives high similarity scores and what constitutes a "good" score. Surprisingly, we find that high similarity scores do not guarantee encoding task-relevant information in a manner consistent with neural data.
  arXiv  Detail & Related papers  (2024-07-09T17:31:47Z)
• Duality of Bures and Shape Distances with Implications for Comparing Neural Representations [6.698235069945606]
  A multitude of (dis)similarity measures between neural network representations have been proposed, resulting in a fragmented research landscape. First, measures such as linear regression, canonical correlations analysis (CCA), and shape distances, all learn explicit mappings between neural units to quantify similarity. Second, measures such as representational similarity analysis (RSA), centered kernel alignment (CKA), and normalized Bures similarity (NBS) all quantify similarity in summary statistics.
  arXiv  Detail & Related papers  (2023-11-19T22:17:09Z)
• Similarity of Neural Network Models: A Survey of Functional and Representational Measures [2.56552999376511]
  Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. We provide a comprehensive overview of two complementary perspectives of measuring neural network similarity: (i) representational similarity, which considers how activations of intermediate layers differ, and (ii) functional similarity, which considers how models differ in their outputs.
  arXiv  Detail & Related papers  (2023-05-10T17:33:48Z)
• Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets [47.20862716252927]
  We use manifold learning to compare the intrinsic geometric structures of different datasets. We define a new theoretically-motivated distance based on a lower bound of the log-Euclidean metric.
  arXiv  Detail & Related papers  (2022-02-03T16:37:23Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• A comprehensive comparative evaluation and analysis of Distributional Semantic Models [61.41800660636555]
  We perform a comprehensive evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. The results show that the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous. We borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models.
  arXiv  Detail & Related papers  (2021-05-20T15:18:06Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)
• Learning from Aggregate Observations [82.44304647051243]
  We study the problem of learning from aggregate observations where supervision signals are given to sets of instances. We present a general probabilistic framework that accommodates a variety of aggregate observations. Simple maximum likelihood solutions can be applied to various differentiable models.
  arXiv  Detail & Related papers  (2020-04-14T06:18:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.