Tootfinder

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Marco Benedetti, Andrej Bogdanov, Enrico M. Malatesta, Marc M\'ezard, Gianmarco Perrupato, Alon Rosen, Nikolaj I. Schwartzbach, Riccardo Zecchina
https://arxiv.org/abs/2506.05197

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Square Wave Perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ``hardness'' properties in the high-dimensional limit at a fixed signal-to-noise ratio $α= O(1)$. In this work, we examine two key aspects of these models. The first is related to the so-called overlap-gap property, that is a disconnectivity feature of the geometry of the solution space of combinatorial optimization problems proven to cause the failure of a large fa…

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Zhentan Zheng
https://arxiv.org/abs/2507.01927 https://arxiv.org/pdf/250…

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Deep neural networks have achieved remarkable results in computer vision tasks. In the early days, Convolutional Neural Networks (CNNs) were the mainstream architecture. In recent years, Vision Transformers (ViTs) have become increasingly popular. In addition, exploring applications of multi-layer perceptrons (MLPs) has provided new perspectives for research into vision model architectures. In this paper, we present evMLP accompanied by a simple event-driven local update mechanism. The proposed…

This https://arxiv.org/abs/2411.15111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
We investigate learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs), comparing traditional Multilayer Perceptrons (MLPs) with fixed and trainable activations against Kolmogorov-Arnold Networks (KANs) that employ learnable basis functions. While PINNs effectively incorporate physical laws into the learning process, they suffer from convergence and spectral bias problems, which limit their applicability to problems with rapid…

Rare dense solutions clusters in asymmetric binary perceptrons -- local entropy via fully lifted RDT
Mihailo Stojnic
https://arxiv.org/abs/2506.19276 https…

Rare dense solutions clusters in asymmetric binary perceptrons -- local entropy via fully lifted RDT
We study classical asymmetric binary perceptron (ABP) and associated \emph{local entropy} (LE) as potential source of its algorithmic hardness. Isolation of \emph{typical} ABP solutions in SAT phase seemingly suggests a universal algorithmic hardness. Paradoxically, efficient algorithms do exist even for constraint densities $α$ fairly close but at a finite distance (\emph{computational gap}) from the capacity. In recent years, existence of rare large dense clusters and magical ability of fast…

QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks
Yannick Werner, Akash Malemath, Mengxi Liu, Vitor Fortes Rey, Nikolaos Palaiodimopoulos, Paul Lukowicz, Maximilian Kiefer-Emmanouilidis
https://arxiv.org/abs/2506.22340

QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks
Kolmogorov Arnold Networks (KANs), built upon the Kolmogorov Arnold representation theorem (KAR), have demonstrated promising capabilities in expressing complex functions with fewer neurons. This is achieved by implementing learnable parameters on the edges instead of on the nodes, unlike traditional networks such as Multi-Layer Perceptrons (MLPs). However, KANs potential in quantum machine learning has not yet been well explored. In this work, we present an implementation of these KAN architec…

USC professor Allison Marsh writes this delightful short article for IEEE Spectrum about Marvin Minsky and Seymour Papert's British counterparts in Cambridge and how, in their case, a document (the Lighthill Report) precipitated cuts in British AI research funding in ways similar to how funding was temporarily cut in the U.S. following MIT's publishing of Minsky and Papert's "Perceptrons" in 1969.
"Freddy the Robot Was the Fall Guy for British AI"

Freddy the Robot Was the Fall Guy for British AI

Its creator lost a power struggle that led to an AI winter in the 1970s

Neural Functions for Learning Periodic Signal
Woojin Cho, Minju Jo, Kookjin Lee, Noseong Park
https://arxiv.org/abs/2506.09526 https://

Neural Functions for Learning Periodic Signal
As function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to learn a nonlinear mapping from a coordinate to its corresponding signal, facilitating the learning of continuous neural representations from discrete data points. Despite notable successes in learning diverse signal types, coordinate-based MLPs often face issues of overfitting and limited generalizability beyond the train…

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Xiaotian Chen, Hongyun Liu, Seyed Sahand Mohammadi Ziabari
https://arxiv.org/abs/2506.09204

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Deep Neural Networks (DNNs) have been proven to be exceptionally effective and have been applied across diverse domains within deep learning. However, as DNN models increase in complexity, the demand for reduced computational costs and memory overheads has become increasingly urgent. Sparsity has emerged as a leading approach in this area. The robustness of sparse Multi-layer Perceptrons (MLPs) for supervised feature selection, along with the application of Sparse Evolutionary Training (SET), i…

This https://arxiv.org/abs/2506.05197 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Square Wave Perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ``hardness'' properties in the high-dimensional limit at a fixed signal-to-noise ratio $α= O(1)$. In this work, we examine two key aspects of these models. The first is related to the so-called overlap-gap property, that is a disconnectivity feature of the geometry of the solution space of combinatorial optimization problems proven to cause the failure of a large fa…

This https://arxiv.org/abs/2411.15111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
We investigate learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs), comparing traditional Multilayer Perceptrons (MLPs) with fixed and trainable activations against Kolmogorov-Arnold Networks (KANs) that employ learnable basis functions. While PINNs effectively incorporate physical laws into the learning process, they suffer from convergence and spectral bias problems, which limit their applicability to problems with rapid…