Streaming Neural Speech Codecs through Time-Invariant Representations
TiCodec separates time-varying and time-invariant speech content for low-latency neural codec applications.
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TiCodec separates time-varying and time-invariant speech content for low-latency neural codec applications.
Iterative pseudo-labeling improves Mandarin-English code-switching ASR by leveraging unlabeled data via semi-supervised training.
Expert evaluation of Evrópuvefur, Iceland's government LLM service for EU referendum Q&A, measuring curated vs. web-search source trustworthiness.
Station F, a Paris-based startup hub founded by French billionaire Xavier Niel, is gearing up for a new edition of its F/ai accelerator program in a bid to strengthen its positioning as a stepping stone for promising AI startups.
Efficient runtime representation for neuro-symbolic learning that decouples program search from parameter calibration via evaluator differentiation.
WeightCLIP learns dataset-aligned latent space for neural network weights using contrastive learning on model and dataset embeddings.
CSympNet-ID: neural framework learning conformal-symplectic maps for linearly damped Hamiltonian systems with long-horizon prediction fidelity.
Program-as-Weights paradigm compiles fuzzy functions from natural-language specs into locally-executable 4B neural artifacts, with 10M-example FuzzyBench dataset.
G-RRM neuro-symbolic framework guides symbolic solvers with symbol-equivariant recurrent reasoning models for constraint satisfaction problems.
Neuron-aware data selection improves LLM self-distillation in annotation-free post-training without domain-specific labels.
Philosophical critique of LLMs as cultural measurement tools, arguing models constitute rather than neutrally measure cultural phenomena.
NeuFS uses neuron-level signals to identify high-value samples for few-shot LLM adaptation, reducing annotation costs in domain specialization.
Applies reinforcement learning perspective to optimize neural quantum state parameterizations via gradient geometry, bridging Adam and stochastic reconfiguration methods.
Single-layer spiking neural network enables in-context learning via dendritic computation, achieving Garg-2022 benchmark with biological plausibility.
Self-explainable operator learning framework reformulates neural architectures as interpretable linear combinations of functional models for physics systems.
H-Score objective enables mutual information-inspired feature learning with proof of invariance to invertible transformations under constrained approximation.
Overview of AI risk assessment and management methodologies across regulatory frameworks including EU AI Act with techniques for technical and ethical risks.
DSGNAR optimization framework solves ill-conditioning in physics-informed neural networks via doubly-sketched Gauss-Newton with adaptive ratio.
Dynamic Neural Graph Encoding uses temporal graphs to analyze neural network weight spaces and inference sequences.
Ensemble machine learning methods detect early-stage Alzheimer's disease biomarkers from neural network analysis.
Improved Fourier Neural Operators for Rayleigh-Bénard convection via time-increment prediction achieve faster inference with 314k parameters.
SA-HGNN applies hyperbolic graph neural networks to EEG data, capturing hierarchical brain connectivity for depression recognition.
GPU-parallel linearization error bounds for robust optimal control of nonlinear and neural network dynamics enable real-time planning with certified constraints.
Neural Certificate Pricing exploits polynomial verification in combinatorial optimization by training neural networks to predict dual prices for certificates.
GAIA operator learning model extends geometry-adaptive neural operators to inverse problems and boundary value problems with domain mismatches.
SEAHORSE: unified benchmark framework standardizing preprocessing, splits, and evaluation for spatiotemporal point process neural models.
Controlled CV-versus-DV quantum neural network comparison on wafer-map defect classification for yield.
Compares strategies for learning cardiac motion priors in implicit neural representations to accelerate optimization.
MultiSynt/MT releases 4.8 trillion tokens of open synthetic parallel pre-training data across 36 European languages via Tower+ and OPUS-MT translation.
Sierra's Natalie Meurer discusses convergence of product and forward-deployed engineers in software development.