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Found 18 papers, 8 papers with code
Towards free-response paradigm: a theory on decision-making in spiking neural networks
The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing.
Efficient Combinatorial Optimization via Heat Diffusion
Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal.
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet.
Learn to integrate parts for whole through correlated neural variability
Sensory perception originates from the responses of sensory neurons, which react to a collection of sensory signals linked to various physical attributes of a singular perceptual object.
A General Description of Criticality in Neural Network Models
Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixture of ordered and disordered patterns.
Digital Twin Brain: a simulation and assimilation platform for whole human brain
In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive.
Modify Training Directions in Function Space to Reduce Generalization Error
We propose theoretical analyses of a modified natural gradient descent method in the neural network function space based on the eigendecompositions of neural tangent kernel and Fisher information matrix.
Probabilistic computation and uncertainty quantification with emerging covariance
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities.
Toward stochastic neural computing
To account for the propagation of correlated neural variability, we derive from first principles a moment embedding for spiking neural network (SNN).
The human digital twin brain in the resting state and in action
We simulate the human brain at the scale of up to 86 billion neurons, i. e., digital twin brain (DTB), which mimics certain aspects of its biological counterpart both in the resting state and in action.
FedDKD: Federated Learning with Decentralized Knowledge Distillation
The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local models to train the global model by approaching the neural network map average based on the metric of divergence defined in the loss function, other than only averaging parameters as done in literature.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
On Fenchel Mini-Max Learning
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
Intelligent Solution System towards Parts Logistics Optimization
Due to the complication of the presented problem, intelligent algorithms show great power to solve the parts logistics optimization problem related to the vehicle routing problem (VRP).
Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years.
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization.
Centralized and Decentralized Global Outer-synchronization of Asymmetric Recurrent Time-varying Neural Network by Data-sampling
In this paper, we discuss the outer-synchronization of the asymmetrically connected recurrent time-varying neural networks.
Stability of Analytic Neural Networks with Event-triggered Synaptic Feedbacks
In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules.
