3 citations · 3 across the 7 of their papers we have counts for
9 papers
Improving Generative Model Self-Training with Geometrically Modified Outputs
Patrick Batsell, Thomas Walker, Richard Baraniuk
Self-training generative models - the continued improvement of a model using its own outputs - is becoming increasingly important as high-quality training data becomes scarce. Howe…
Behavioral Monitoring of JEPA World Models with Jacobian Centroids
Thomas Walker, Randall Balestriero, Richard Baraniuk
Detecting failures in World Model (WM)-based planning requires monitoring whether the model is behaviorally aligned with the current task, which in turn requires studying its inter…
The Geometric Structure of Models Learning Sparse Data
Thomas Walker, T. Mitchell Roddenberry, Ahmed Imtiaz Humayun +2
The manifold hypothesis (MH) is often used to explain how machine learning can overcome the curse of dimensionality. However, the MH is only applicable in regimes where the trainin…
The Linear Centroids Hypothesis: Features as Directions Learned by Local Experts
Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero +1
The Linear Representation Hypothesis (LRH) identifies features of a trained deep network (DN) as linear directions in the activation spaces, i.e., output spaces of intermediate lay…
GrokAlign: Geometric Characterisation and Acceleration of Grokking
Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero +1
A key challenge for the machine learning community is to understand and accelerate the training dynamics of deep networks that lead to delayed generalisation and emergent robustnes…
Concept Boundary Vectors
Thomas Walker
Machine learning models are trained with relatively simple objectives, such as next token prediction. However, on deployment, they appear to capture a more fundamental representati…
Tightening the Evaluation of PAC Bounds Using Formal Verification Results
Thomas Walker, Alessio Lomuscio
Probably Approximately Correct (PAC) bounds are widely used to derive probabilistic guarantees for the generalisation of machine learning models. They highlight the components of t…
Spherical Feature Pyramid Networks For Semantic Segmentation
Thomas Walker, Varun Anand, Pavlos Andreadis
Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean p…
Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fields
Thomas Walker, Octave Mariotti, Amir Vaxman +1
We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain. We apply t…