A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified understanding of neural representation similarity measures.
Deconfounds neural network representation similarity metrics to improve consistency and accuracy.
Geometric stability measures neural network robustness, distinguishing from similarity metrics.
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting colorectal cancer survival curves for patients in the state of Iowa, sp…
Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for comparing neural network representations based on canonical correlation analysis (CCA). We show that CCA belongs to a family of statistics for mea…
The paper analyzes MAML's representation using RSA, revealing that feature reuse is not the primary reason for its success.
Novel tRSA combines geometry and topology for brain and model analysis.
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distribution…
New measures link neural representation geometry to decoding ability.
New method learns behavioral representations from mobility data.
Unified toolkit for comparing neural representations using SRTD and NTS.
Centered Kernel Alignment (CKA) was recently proposed as a similarity metric for comparing activation patterns in deep networks. Here we experiment with the modified RV-coefficient (RV2), which has very similar properties as CKA while being less sensitive to dataset size. We compare the representations of networks that…
A new method identifies similar mutual funds using graph learning.
MSA compares neural representations' intrinsic geometry for better understanding.
The paper studies how neural networks evolve representations, finding a unique fixed point for nonlinear activations.
New framework to test neural network representation similarity measures.
New findings on representation changes in transfer learning.
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical pr…
This paper investigates how forgetting affects neural network representations and stabilizes deeper layers.
PRESTO maps latent representations across diverse ML models.
Defines metrics to compare neural network representations.
Representations of -algebras are realized on section spaces of holomorphic homogeneous vector bundles. The corresponding section spaces are investigated by means of a new notion of reproducing kernel, suitable for dealing with involutive diffeomorphisms defined on the base spaces of the bundles. Applications of th…
Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorithm: leveraging availability of pairs of semantically "similar" data points and "n…
Proposes a method to compare neural networks using feature and gradient vectors.
Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks. Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly, e…
New findings clarify the link between distributional closeness and representational similarity.
Paper analyzes how contrastive learning structures learned representations.
Simplifies fair PCA with fast, efficient solution.
Graph neural networks improve with edge similarity constraints in RNA structure analysis.
The autoencoder is an artificial neural network model that learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar…
New method learns low-dimensional representations of AI-generated treatments.
Representational Similarity Analysis (RSA) aims to explore similarities between neural activities of different stimuli. Classical RSA techniques employ the inverse of the covariance matrix to explore a linear model between the neural activities and task events. However, calculating the inverse of a large-scale covarian…
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components. Unfortunately, the straightforward vector encoding of k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse yet, the distance between any pair of one-hot vectors is equidistant. This is partic…
Feature normalization prevents collapse in non-contrastive learning dynamics.
AI models aligned with human vision perform well on few data tasks.
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an altern…
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such a…
DORA analyzes deep neural networks' internal representations to detect spurious correlations.
Deep Belief Network predicts lncRNA-disease associations with high accuracy.
New metric for disentangling multivariate representations, accounting for more complex entanglements.
Diffusion Maps framework is a kernel based method for manifold learning and data analysis that defines diffusion similarities by imposing a Markovian process on the given dataset. Analysis by this process uncovers the intrinsic geometric structures in the data. Recently, it was suggested to replace the standard kernel …
Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM). However, how to p…
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogen…
Extract common latent factors from graphs for better representation learning.
We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor…
Logit distance bounds representational similarity of models.
Task loss matching misrepresents similarity between neural network layers.