Trinity Chung

@trinityjchung.com

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MS @ Carnegie Mellon University Robotics Institute BS @ UC Berkeley Computer Science

Comparison of neural fit (noise-corrected RSA Pearson’s r) across models. The mean animal-to-animal score is 0.18 and the maximum between all pairs of animals is 1.16. The leftmost “a2a” bar represents the mean animal-to-animal neural consistency score. The lighter-colored left bar represents the randomly initialized version for every model.
Types of data augmentations applied to SSL models. Given a temporal tactile input over time T, our tactile augmentation vertically, horizontally, temporally flips, and rotates the features, while traditional image augmentation introduces Gaussian noise, color jitter, and grayscale. The tactile augmentations were effective in improving both the neural fit and task performance. The models were unable to be trained with image augmentations.
We use average mouse whisker array measurements to recreate a whisker model in sim. (II) Objects are whisked in simulation using WHISKiT (from Hartmann's lab) resulting in (III) force and torque data for sweeping 9981 ShapeNet objects of 117 categories with various sweep augmentations. The augmentations vary the (1) speed, (2) height, (3), rotation, and (4) distance of the objects relative to the whisker array. We constructed two datasets: a large, low-fidelity set with more sweep augmentations, and a small, high-fidelity set with fewer augmentations.
The Encoder-Attender-Decoder (EAD) architecture, with task objectives being supervised categorization, self-supervised learning (SimCLR, SimSiam, autoencoding). The ConvRNN encoder includes self-recurrence at each layer where we vary different RNNs.
Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain