8 citations · 12 across the 7 of their papers we have counts for
9 papers
A Mathematical Theory of the Jacobian Lens
Mitchell Ostrow, Daoyuan Qian, Toni Liu +1
Recent work from Anthropic introduced a new interpretability tool, called the Jacobian lens (J-lens, Gurnee et al., 2026). This method identified linear directions in the hidden st…
Traversing the solution space of neural networks with Hessian Null Space Continuation
Ann Huang, Mitchell Ostrow, Zhouyang Lu +3
On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters. Many of these solutions are mode-connected…
Fast dynamical similarity analysis
Arman Behrad, Mitchell Ostrow, Mohammad Taha Fakharian +3
Understanding how nonlinear dynamical systems (e.g., artificial neural networks and neural circuits) process information requires comparing their underlying dynamics at scale, acro…
InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics
Ann Huang, Mitchell Ostrow, Satpreet H. Singh +3
In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on t…
Characterizing control between interacting subsystems with deep Jacobian estimation
Adam J. Eisen, Mitchell Ostrow, Sarthak Chandra +3
Biological function arises through the dynamical interactions of multiple subsystems, including those between brain areas, within gene regulatory networks, and more. A common appro…
How Diffusion Models Learn to Factorize and Compose
Qiyao Liang, Ziming Liu, Mitchell Ostrow +1
Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set, demonstrating the ability to \texti…
Delay Embedding Theory of Neural Sequence Models
Mitchell Ostrow, Adam Eisen, Ila Fiete
To generate coherent responses, language models infer unobserved meaning from their input text sequence. One potential explanation for this capability arises from theories of delay…
Bridging Associative Memory and Probabilistic Modeling
Rylan Schaeffer, Nika Zahedi, Mikail Khona +9
Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and r…
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
Mitchell Ostrow, Adam Eisen, Leo Kozachkov +1
How can we tell whether two neural networks utilize the same internal processes for a particular computation? This question is pertinent for multiple subfields of neuroscience and…