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Organizational Regularities in Recurrent Neural Networks
Claus Metzner, Achim Schilling, Andreas Maier, Patrick Krauss
https://arxiv.org/abs/2505.22047 htt…

Organizational Regularities in Recurrent Neural Networks
Previous work has shown that the dynamical regime of Recurrent Neural Networks (RNNs) - ranging from oscillatory to chaotic and fixpoint behavior - can be controlled by the global distribution of weights in connection matrices with statistically independent elements. However, it remains unclear how network dynamics respond to organizational regularities in the weight matrix, as often observed in biological neural networks. Here, we investigate three such regularities: (1) monopolar output weigh…

RCNet: $\Delta\Sigma$ IADCs as Recurrent AutoEncoders
Arnaud Verdant, William Guicquero, J\'er\^ome Chossat
https://arxiv.org/abs/2506.16903 https://…

RCNet: $ΔΣ$ IADCs as Recurrent AutoEncoders
This paper proposes a deep learning model (RCNet) for Delta-Sigma ($ΔΣ$) ADCs. Recurrent Neural Networks (RNNs) allow to describe both modulators and filters. This analogy is applied to Incremental ADCs (IADC). High-end optimizers combined with full-custom losses are used to define additional hardware design constraints: quantized weights, signal saturation, temporal noise injection, devices area. Focusing on DC conversion, our early results demonstrate that $SNR$ defined as an Effective Numb…

This https://arxiv.org/abs/2505.22083 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
In this work, we introduce the first type of non-Euclidean neural quantum state (NQS) ansatz, in the form of the hyperbolic GRU (a variant of recurrent neural networks (RNNs)), to be used in the Variational Monte Carlo method of approximating the ground state wavefunction for quantum many-body systems. In particular, we examine the performances of NQS ansatzes constructed from both conventional or Euclidean RNN/GRU and from hyperbolic GRU in the prototypical settings of the one- and two-dimensi…

Transient Dynamics in Lattices of Differentiating Ring Oscillators
Peter DelMastro, Arjun Karuvally, Hananel Hazan, Hava Siegelmann, Edward Rietman
https://arxiv.org/abs/2506.07253

Transient Dynamics in Lattices of Differentiating Ring Oscillators
Recurrent neural networks (RNNs) are machine learning models widely used for learning temporal relationships. Current state-of-the-art RNNs use integrating or spiking neurons -- two classes of computing units whose outputs depend directly on their internal states -- and accordingly there is a wealth of literature characterizing the behavior of large networks built from these neurons. On the other hand, past research on differentiating neurons, whose outputs are computed from the derivatives of …

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading
Alireza Ostadrahimi, Amir Teimouri, Kshitiz Upadhyay, Guoqiang Li
https://arxiv.org/abs/2507.12683

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading
We propose a general hybrid physics-informed machine learning framework for modeling nonlinear, history-dependent viscoelastic behavior under multiaxial cyclic loading. The approach is built on a generalized internal state variable-based visco-hyperelastic constitutive formulation, where stress is decomposed into volumetric, isochoric hyperelastic, and isochoric viscoelastic components. Gaussian Process Regression (GPR) models the equilibrium response, while Recurrent Neural Networks (RNNs) wit…

Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
Yi Zhang, Yidong Zhao, Qian Tao
https://arxiv.org/abs/2507.00582

Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
Deformable medical image registration is traditionally formulated as an optimization problem. While classical methods solve this problem iteratively, recent learning-based approaches use recurrent neural networks (RNNs) to mimic this process by unrolling the prediction of deformation fields in a fixed number of steps. However, classical methods typically converge after sufficient iterations, but learning-based unrolling methods lack a theoretical convergence guarantee and show instability empir…

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Wonung Kim, Yubin Lee, Yoonsung Kim, Jinwoo Hwang, Seongryong Oh, Jiyong Jung, Aziz Huseynov, Woong Gyu Park, Chang Hyun Park, Divya Mahajan, Jongse Park
https://arxiv.org/abs/2507.10178

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Transformers are the driving force behind today's Large Language Models (LLMs), serving as the foundation for their performance and versatility. Yet, their compute and memory costs grow with sequence length, posing scalability challenges for long-context inferencing. In response, the algorithm community is exploring alternative architectures, such as state space models (SSMs), linear attention, and recurrent neural networks (RNNs), which we refer to as post-transformers. This shift presents a k…

This https://arxiv.org/abs/2502.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Dynamical phases of short-term memory mechanisms in RNNs
Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we introduce two unique mechanisms that can support this f…