First integrated spiking controller combining bipedal locomotion and arm control on a full-scale humanoid via NEF, SPA, and basal ganglia, validated in Nengo-Isaac Sim co-simulation.
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A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
A new sequential interaction framework lets LLMs propose questions to forums, with simulations on real Stack Exchange data showing players can reach roughly half the utility of an ideal full-information scenario despite incentive misalignment.
Synthetic experiments reveal that class-dependent effects appear in both perturbation-based and ground-truth evaluations of time series feature attributions, often producing contradictory rankings of attribution quality due to differences in feature amplitude or temporal extent between classes.
TEMPO-Diffusion is a targeted backdoor attack framework for diffusion models that uses time-conditioned triggers to poison class-specific synthetic data, achieving high attack success in downstream classifiers.
Three optimized MPC protocols for privacy-preserving vertical federated learning that support global and global-local updates while reducing computation versus naive full-MPC delegation.
A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.
Introduces the Euler (a,b)-logarithm as a unifying kernel for generalized entropies and applies it to generalized exponentiated gradient, mirror descent, backpropagation, and natural gradient descent in machine learning.
citing papers explorer
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A Spiking Neural Architecture for Coordinating Arm and Locomotor Control
First integrated spiking controller combining bipedal locomotion and arm control on a full-scale humanoid via NEF, SPA, and basal ganglia, validated in Nengo-Isaac Sim co-simulation.
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums
A new sequential interaction framework lets LLMs propose questions to forums, with simulations on real Stack Exchange data showing players can reach roughly half the utility of an ideal full-information scenario despite incentive misalignment.
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Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
Synthetic experiments reveal that class-dependent effects appear in both perturbation-based and ground-truth evaluations of time series feature attributions, often producing contradictory rankings of attribution quality due to differences in feature amplitude or temporal extent between classes.
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TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
TEMPO-Diffusion is a targeted backdoor attack framework for diffusion models that uses time-conditioned triggers to poison class-specific synthetic data, achieving high attack success in downstream classifiers.
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Secure and Privacy-Preserving Vertical Federated Learning
Three optimized MPC protocols for privacy-preserving vertical federated learning that support global and global-local updates while reducing computation versus naive full-MPC delegation.
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Scalable Construction of Spiking Neural Networks using up to thousands of GPUs
A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.
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Generalized Euler Logarithm and its Applications in Machine Learning: Natural Gradient, Backpropagation, Generalized EG, Mirror Descent and OLPS
Introduces the Euler (a,b)-logarithm as a unifying kernel for generalized entropies and applies it to generalized exponentiated gradient, mirror descent, backpropagation, and natural gradient descent in machine learning.