3 papers
cs.LG2026
On the Role of Depth in the Expressivity of RNNs
Maude Lizaire, Michael Rizvi-Martel, Éric Dupuis +1
The benefits of depth in feedforward neural networks are well known: composing multiple layers of linear transformations with nonlinear activations enables complex computations. Wh…
quant-ph2025
FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau
Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, wher…
math.NA2025
Numerical PDE solvers outperform neural PDE solvers
Patrick Chatain, Michael Rizvi-Martel, Guillaume Rabusseau +1
We present DeepFDM, a differentiable finite-difference framework for learning spatially varying coefficients in time-dependent partial differential equations (PDEs). By embedding a…