46 citations · 86 across the 10 of their papers we have counts for
10 papers
Reshaping Global Loop Structure to Accelerate Local Optimization by Smoothing Rugged Landscapes
Timothee Leleu, Sam Reifenstein, Atsushi Yamamura +1
Probabilistic graphical models with frustration exhibit rugged energy landscapes that trap iterative optimization dynamics. These landscapes are shaped not only by local interactio…
The geometry and dynamics of annealed optimization in the coherent Ising machine with hidden and planted solutions
Federico Ghimenti, Adithya Sriram, Atsushi Yamamura +2
The coherent Ising machine (CIM) is a nonconventional hardware architecture for finding approximate solutions to large-scale combinatorial optimization problems. It operates by ann…
Fooling LLM graders into giving better grades through neural activity guided adversarial prompting
Atsushi Yamamura, Surya Ganguli
The deployment of artificial intelligence (AI) in critical decision-making and evaluation processes raises concerns about inherent biases that malicious actors could exploit to dis…
Geometric landscape annealing as an optimization principle underlying the coherent Ising machine
Atsushi Yamamura, Hideo Mabuchi, Surya Ganguli
Given the fundamental importance of combinatorial optimization across many diverse application domains, there has been widespread interest in the development of unconventional phys…
High-Dimensional Non-Convex Landscapes and Gradient Descent Dynamics
Tony Bonnaire, Davide Ghio, Kamesh Krishnamurthy +3
In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Ou…
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
Feng Chen, Daniel Kunin, Atsushi Yamamura +1
In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives overly expressive networks to much simpler subnetworks, thereby dramatically reducin…
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
Daniel Kunin, Atsushi Yamamura, Chao Ma +1
In this work, we explore the maximum-margin bias of quasi-homogeneous neural networks trained with gradient flow on an exponential loss and past a point of separability. We introdu…
Onset of non-Gaussian quantum physics in pulsed squeezing with mesoscopic fields
Ryotatsu Yanagimoto, Edwin Ng, Atsushi Yamamura +6
We study the emergence of non-Gaussian quantum features in pulsed squeezed light generation with a mesoscopic number (i.e., dozens to hundreds) of pump photons. Due to the strong o…
Nonlinear Quantum Behavior of Ultrashort-Pulse Optical Parametric Oscillators
Tatsuhiro Onodera, Edwin Ng, Chris Gustin +6
The quantum features of ultrashort-pulse optical parametric oscillators (OPOs) are investigated theoretically in the nonlinear regime near and above threshold. Viewing the pulsed O…
A Quantum Model for Coherent Ising Machines: Discrete-time Measurement Feedback Formulation
Atsushi Yamamura, Kazuyuki Aihara, Yoshihisa Yamamoto
Recently, the coherent Ising machine (CIM) as a degenerate optical parametric oscillator (DOPO) network has been researched to solve Ising combinatorial optimization problems. We f…