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This paper proposes a dual-domain fused LSTM (DDF-LSTM) model for time-dependent reliability analysis that integrates time-independent random variables and stochastic processes to efficiently estimate failure probabilities via Monte Carlo simulation.
This survey provides a unifying perspective on verification methods for reinforcement learning policies, introducing a taxonomy along three axes (verification paradigm, temporal scope, and guarantee strength) and identifying emerging directions.
This paper proposes a semiparametric framework for stochastic fundamental diagram modeling that combines physically-constrained functional forms with neural network structures to capture complex traffic flow patterns and uncertainty, demonstrating superior performance on real-world datasets.
Google Research shows that the creativity of diffusion models is a mathematical consequence of neural network regularization causing score smoothing and interpolation, demystifying their ability to generate novel data rather than just memorize.
This paper proposes a latency-oriented neural network learning method that uses zerorized batch normalization to optimize DNN architectures for edge systems under strict latency constraints. Experiments show significant latency reduction with minimal accuracy loss on NVIDIA Jetson devices.
Introduces cross-trajectory chimera interventions to dissociate the roles of weight magnitude and direction in grokking, showing that direction carries transferable circuit identity while norm affects susceptibility to overwriting.
A model is proposed to fill in missing depth data from depth cameras when encountering transparent surfaces like glass walls, addressing a common sensor limitation.
An open source tool for validating the shapes of neural networks has been built and shared by the author.
Proposes M-QCDNet, a structure-aware deep learning architecture that embeds multilayer Q-matrices for enhanced psychometric interpretability in cognitive diagnosis.
A virtual creature trained with reinforcement learning has been given fur and sound, with the sound generated directly from neural network activations. Trained using JAX and MuJoCo, and rendered with three.js and WebGPU, it presents a realistic effect of crawling on the floor, blending art and research.
FLYNN is a recurrent neural network derived from the fruit fly brain connectome for vision-based robot navigation. It achieves comparable performance to handcrafted networks while showing superior robustness to out-of-distribution data and sensory loss.
Introduces Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), a hybrid neural architecture for explainable cybersecurity risk assessment in open-source ecosystems, using 80 interpretable neurons across 12 layers with hard constraints for interpretability.
This paper proposes Dualformer, a dual-channel neural network architecture based on transformers, designed for efficient feature extraction from complex-valued signals in blind communication analysis tasks such as automatic modulation recognition, signal scheme recognition, and signal structure parsing. Extensive experiments show consistent performance improvements over existing methods.
Alexia Jolicoeur-Martineau was the solo author on TRM, pushing recursion limits on a small 2-layer network, achieving impressive performance on ARC-AGI. She has joined Microsoft as a Principal researcher.
This paper presents CA-NKCF, a novel distributed latent state estimator combining partial domain knowledge with deep neural networks, achieving robust performance without noise statistics knowledge, outperforming traditional filters in linear, chaotic, and wireless tracking environments.
This paper introduces a unified decision-theoretic pretraining framework for neural network-based time series estimators, trained on stratified simulations to approximate near-optimal decision rules. Experiments show that the resulting estimators outperform traditional methods like maximum likelihood estimation on both synthetic and real-world benchmarks.
A tweet promoting a guide on building a neural network from scratch in C++, aimed at providing a clear and practical explanation of how neural networks work.
New research indicates that the apparent 'global convergence' in scaled AI models is actually a statistical illusion caused by selection bias in model width and depth, and disappears once calibrated.
Garry Tan shares the complete architecture of GBrain, an AI model.
A set of four cards covering the core concepts of neural networks: neuron, forward pass, activations, and backpropagation, aimed at helping learners understand how models from perceptrons to transformers work.