Neural and Evolutionary Computing Papers
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New Neural and Evolutionary Computing submissions to https://t.co/PCh9ZUDtib (not affiliated with https://t.co/PCh9ZUDtib)
Joined November 2010
Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers.
arxiv.org
Pretrained language models (LMs) are prone to arithmetic errors. Existing work showed limited success in probing numeric values from models' representations, indicating that these errors can be...
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Stochastic Forward-Forward Learning through Representational Dimensionality Compression.
arxiv.org
The Forward-Forward (FF) algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function to guide learning. Existing...
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Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks.
arxiv.org
Growing evidence suggests that synaptic weights in the brain follow heavy-tailed distributions, yet most theoretical analyses of recurrent neural networks (RNNs) assume Gaussian connectivity. We...
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LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization.
arxiv.org
Constrained multi-objective optimization problems (CMOPs) frequently arise in real-world applications where multiple conflicting objectives must be optimized under complex constraints. Existing...
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Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy.
arxiv.org
Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI)...
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Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity.
arxiv.org
Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted much interest. In...
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Exploring the Limitations of Layer Synchronization in Spiking Neural Networks.
arxiv.org
Neural-network processing in machine learning applications relies on layer synchronization. This is practiced even in artificial Spiking Neural Networks (SNNs), which are touted as consistent with...
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An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning.
arxiv.org
Placental abruption is a severe complication during pregnancy, and its early accurate diagnosis is crucial for ensuring maternal and fetal safety. Traditional ultrasound diagnostic methods heavily...
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Hybrid Genetic Algorithm for Optimal User Order Routing: Multi-Objective Solver Optimization in CoW Protocol Batch Auctions.
arxiv.org
CoW Protocol batch auctions aggregate user intents and rely on solvers to find optimal execution paths that maximize user surplus across heterogeneous automated market makers (AMMs) under...
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REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models.
arxiv.org
Large Language Models (LLMs) are used for Register-Transfer Level (RTL) code generation, but they face two main challenges: functional correctness and Power, Performance, and Area (PPA)...
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Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks.
arxiv.org
Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have...
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Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies.
arxiv.org
Biological circuits have evolved to incorporate multiple modules that perform similar functions. In the fly olfactory circuit, both lateral inhibition (LI) and neuronal spike frequency adaptation...
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DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization.
arxiv.org
Designing effective black-box optimizers is hampered by limited problem-specific knowledge and manual control that spans months for almost every detail. In this paper, we present DesignX, the...
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CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs.
arxiv.org
Solving time-dependent Partial Differential Equations (PDEs) using a densely discretized spatial domain is a fundamental problem in various scientific and engineering disciplines, including...
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Depth-Bounds for Neural Networks via the Braid Arrangement.
arxiv.org
We contribute towards resolving the open question of how many hidden layers are required in ReLU networks for exactly representing all continuous and piecewise linear functions on $\mathbb{R}^d$....
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Frequency Cam: Imaging Periodic Signals in Real-Time.
arxiv.org
Due to their high temporal resolution and large dynamic range, event cameras are uniquely suited for the analysis of time-periodic signals in an image. In this work we present an efficient and...
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Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control.
arxiv.org
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware, making them attractive for Reinforcement Learning (RL) in resource-constrained edge...
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Experimental differentiation and extremization with analog quantum circuits.
arxiv.org
Solving and optimizing differential equations (DEs) is ubiquitous in both engineering and fundamental science. The promise of quantum architectures to accelerate scientific computing thus...
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[RETRACTED]Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks.
arxiv.org
[RETRACTED]Data increasingly abounds, but distilling their underlying relationships down to something interpretable remains challenging. One approach is genetic programming, which `symbolically...
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Emergence of Internal State-Modulated Swarming in Multi-Agent Patch Foraging System.
arxiv.org
Active particles are entities that sustain persistent out-of-equilibrium motion by consuming energy. Under certain conditions, they exhibit the tendency to self-organize through coordinated...
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