6 citations · 13 across the 12 of their papers we have counts for
12 papers
How Data Augmentation Shapes Neural Representations
Tianxiao He, Alex H. Williams, Sarah E. Harvey
Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains poorly understood. In this w…
Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion
Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko +1
Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predict…
Partial Soft-Matching Distance for Neural Representational Comparison with Partial Unit Correspondence
Chaitanya Kapoor, Alex H. Williams, Meenakshi Khosla
Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural representations. We extend the soft-matc…
Quasi Monte Carlo methods enable extremely low-dimensional deep generative models
Miles Martinez, Alex H. Williams
This paper introduces quasi-Monte Carlo latent variable models (QLVMs): a class of deep generative models that are specialized for finding extremely low-dimensional and interpretab…
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Victor Geadah, Amin Nejatbakhsh, David Lipshutz +2
Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical S…
Comparing noisy neural population dynamics using optimal transport distances
Amin Nejatbakhsh, Victor Geadah, Alex H. Williams +1
Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity i…
What Representational Similarity Measures Imply about Decodable Information
Sarah E. Harvey, David Lipshutz, Alex H. Williams
Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or ``decoders'' th…
Discriminating image representations with principal distortions
Jenelle Feather, David Lipshutz, Sarah E. Harvey +2
Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global structure can have st…
Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
Brett W. Larsen, Tamara G. Kolda, Anru R. Zhang +1
The canonical polyadic (CP) tensor decomposition decomposes a multidimensional data array into a sum of outer products of finite-dimensional vectors. Instead, we can replace some o…
Duality of Bures and Shape Distances with Implications for Comparing Neural Representations
Sarah E. Harvey, Brett W. Larsen, Alex H. Williams
A multitude of (dis)similarity measures between neural network representations have been proposed, resulting in a fragmented research landscape. Most of these measures fall into on…
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning
Meenakshi Khosla, Alex H. Williams
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that…
Estimating Shape Distances on Neural Representations with Limited Samples
Dean A. Pospisil, Brett W. Larsen, Sarah E. Harvey +1
Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning. Although many methods have be…