Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures
Consecutive Posterior Fusion extends diffusion posterior sampling by reusing prior measurement corrections for ill-posed inverse imaging problems.
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Consecutive Posterior Fusion extends diffusion posterior sampling by reusing prior measurement corrections for ill-posed inverse imaging problems.
Neuro-Physical Inverter couples ensemble conditioning with residual neural networks for 1D magnetotelluric geophysical inversion.
Neural architecture search optimizes parameterized quantum circuit design integrated with classical deep learning for image classification tasks.
KV²: query-agnostic KV-cache compression via selective reconstruction for reusable long-context inference.
ImmuneAgent integrates multimodal reasoning, meta-learning, and wet-lab feedback for broadly neutralizing antibody discovery from BCR repertoires across virus families.
AMSC framework uses Wasserstein task embeddings to find and compose relevant prior policies for continuous adaptation in lifelong RL.
Empirical study of trade-offs between structured pruning and fault tolerance in DNNs for space applications with bit-level robustness.
Graph neural networks on tissue-specific protein interactomes show effective resistance degenerates to inverse degree, limiting uncertainty quantification signal.
Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a principled alternative to heuristic scoring by learning data weights from a target validation objective, but existing methods…
A federal judge has dismissed a pair of antitrust lawsuits filed by Chegg and Rolling Stone parent company Penske Media Corporation, which accused Google of driving away web traffic with its AI-powered search features, as reported earlier by Reuters. US District Judge Amit…
Adding in a second neural network that guesses the identity of hidden pieces was key.
Port-Hamiltonian neural networks extended with Bregman divergences to model systems with multiple stable equilibria.
European Crisis Management Ontology (ECMO) defines modular OWL-based schema for cross-sectoral disaster management.
Clifford Sheaf Neural Networks apply Clifford algebras to equivariant graph learning with K-term sandwich restriction maps.
PPNAS combines prediction-powered inference to fuse sparse performance labels with noisy zero-cost proxies for efficient neural architecture search.
Simon Willison visits NYC Museum exhibition of Joe Macken's 50x27ft architectural model.
Brain-to-text decoding improvements from d'Ascoli et al. are largely reproducible via timing shortcuts, not actual neural signals.
Looped Diffusion Transformer scales computation by reusing Transformer blocks within denoising steps to improve text-to-image generation without larger models.
Green's operator analyzes perturbation routing in recurrent networks to study task-to-task and time-to-time computation reuse.
Action Experience Dictionary enables World Action Models to reuse manipulation skills across tasks by encoding historical trajectories into shared action embeddings.
RL-guided graph transformations optimize sparse triangular solve kernels by learning heuristics instead of manual design.
Neurosymbolic regularization via prototypes for rank-constrained tensor networks under label scarcity on hyperspectral data.
Learnable subspace projection for transformer compression via error propagation analysis, improving on local closed-form criteria.
Training-free affinity fusion integrates neural diarization partitions with embedding-based speaker diarization systems.
T-Router uses parameter-efficient RL with thalamic routing to improve reasoning via reuse of learned computations.
LIFT architecture enabling recurrent state propagation across transformer layers during pretraining, eliminating feed-forward-only bottleneck for information reuse during generation.
Stochastic world models as verifiable perception surrogates for vision-based neural feedback systems, replacing GANs.
NeuronEye plug-in constructs sparse concept-level neuron vocabulary from VLMs to isolate and modulate query-relevant visual evidence.
Neural topology optimization for ship structures using Kolmogorov-Arnold networks to minimize vibration-induced compliance.
Hessian Null Space Continuation method maps mode-connected solutions in neural network weight space to study internal representation degeneracy.