Neural operators approximate strongly continuous convex monotone semigroups
Universal approximation theorem for neural operators approximating convex monotone semigroups via Chernoff methods.
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Universal approximation theorem for neural operators approximating convex monotone semigroups via Chernoff methods.
Pseudo-triplet construction pipeline enables direction-following text-to-speech generation without paired training data of script modifications and delivery directions.
Theoretical analysis of SGD dynamics via percolation modeling reveals discrete phase transitions in neural network subnetwork merging.
AgentScope: neuro-symbolic framework for diagnosing LLM agent failures via behavioral abstractions and structured analysis.
Analyzes gradient-optimizer mismatch in Physics-Informed Neural Networks, showing conflict-free gradient surgery directions may not preserve properties post-optimizer transformation.
Linear Reusable Neural Bases Architecture (LRNBA) achieves model compression by representing network blocks as linear combinations of shared bases.
Edge-girth structural descriptor for GNNs addresses Weisfeiler-Leman limitations without precommitting to subgraph size.
TRIAGE three-level routing framework reduces LLM agent token consumption by reusing historical trajectories as reusable skills.
Physics-Informed Neural Networks applied to Ground-Penetrating Radar data prediction for subsurface anomaly forecasting.
NeuSOGA framework extracts explicit symbolic mathematical representations from geometric observations using neuro-symbolic learning.
Dual Neural-Calibrated IMM method improves lane-change intention recognition for autonomous driving by incorporating driving context via neural calibration.
Neural Symbolic Regression framework combines neural nets as preconditioners with sparse regression, addressing scalability and noise sensitivity of genetic programming methods.
Sharp approximation rates derived for neural networks using low-dimensional latent parameter generators with affine mappings to network weights.
Template Model Builder (TMB) framework simplifies implementation of neural network mixed-effects models via automatic differentiation.
Analysis showing sycophantic agreement emerges unintentionally from contrastive preference optimization; demonstrates transfer across model families via OLMo 3.
Statistical analysis of 13 neural audio codec token sequences across architectures and noise conditions using Zipf/Heaps metrics.
Clinical NLP framework uses automatic language analysis to augment psychiatrist judgments of patient experience in therapeutic conversations.
Sparse competition-based training induces modular specialization in neural networks while maintaining baseline accuracy.
Explosive growth comes with a new regulatory burden in the European Union.
OpenAI will soon be held accountable for mitigating risks related to ChatGPT's impact on minors, user mental health, and the spread of illegal content in the European Union. That's because ChatGPT is now considered a Very Large Online Search Engine under the EU's Digital…
MolLedger: graph neural network with per-atom attributions for ADME property prediction in drug discovery.
CNN-BiLSTM architecture with Bayesian optimization for battery state-of-health estimation in energy systems.
Trajectory-Initialized Neural Double Q-Routing for overhead hoist transport systems in semiconductor fabs using adaptive routing under resource contention.
Investors, founders, and operators from across Europe arrived for the annual Nordic TechBBQ conference to talk about how humans can have agency over AI.
Physics-informed neural networks for nonlocal differential equations on quantum graphs using PyTorch implementation.
Comprehensive survey of neural network optimizers in 2025–2026 covering temporal estimation, geometry, scheduling, and distributed systems.
DWT_AlexNet_DNN hybrid framework combines discrete wavelet transform and deep learning for improved texture image classification across industrial and medical domains.
Split conformal framework provides pointwise uncertainty quantification guarantees for neural operator surrogates via calibrated residual bands and spatial quantiles.
Real-time virtual circuits for tokamak plasma control on MAST Upgrade using neural network surrogates instead of offline lookup tables.
Euclidean Fourier Neural Operators address domain-transfer failure in standard FNOs by making spectral weights grid and domain-independent.