We introduce BIRD (Behavior Induction via Representation-structure Distillation), a flexible framework for transferring aligned behavior by matching the internal representation structure of a student model to that of a teacher.
We compare two complementary approaches to semantic preprocessing in immersive virtual reality: *SemanticEdges*, which highlights all relevant objects at once, and *SemanticRaster*, which staggers object categories over time to reduce visual clutter.
We propose a Gaussian Process Regression (GPR) framework to predict perceptual thresholds at unsampled locations while leveraging uncertainty estimates to guide adaptive sampling.
We evaluate HILO using sighted participants viewing simulated prosthetic vision to assess its ability to optimize stimulation strategies under realistic conditions.
We propose a novel temporal-digital architecture that encodes ANN weights as delays and activations as signal arrival times, enabling full ANN execution with temporal reuse, noise-tolerant summation, and hybrid memory, achieving up to 11× energy and 4× latency improvements over SNNs, and 3.5× energy savings over 8-bit digital systolic arrays.
We introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.
We conducted user studies evaluating eye tracking on the Magic Leap One, the HoloLens 2, and the Meta Quest Pro to show how locomotion influences eye tracking performance in these headsets.
We systematically incorporated neuroscience-derived architectural components into CNNs to identify a set of mechanisms and architectures that comprehensively explain neural activity in V1.