An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
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cs.LG 27 cs.CV 9 cs.AI 6 cs.CL 3 eess.SP 3 physics.ao-ph 3 q-bio.NC 3 quant-ph 3 astro-ph.IM 2 astro-ph.SR 2polarities
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ffortissimo is a JAX-based freeform forward-modeling pipeline that fits complex dust distributions and infers scattering properties in KLIP-reduced images of circumstellar disks such as HR 4796A.
eCNNTO applies an element-wise CNN with residual connections and final-stage training data to accelerate density-based topology optimization while generalizing across boundary conditions, loads, geometries, and mesh sizes.
Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
Spatial multiplexing in optical neural networks is repurposed as a trainable representational coordinate, demonstrated in multi-layer architectures for image classification, regression, and hybrid vision-language captioning with over one million optical phase parameters.
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
In a combinatorial toy setting, winning lottery tickets preserve families of compatible feature locations in early feature space that balance proximity to final codes with low interference, rather than specific weight subnetworks.
DualTCN is the first deep-learning model for time-domain marine CSEM inversion that regresses four earth parameters, achieves high accuracy on simulated data, and runs up to 21,000 times faster than classical optimizers.
Broximal Alignment is a novel condition under which the Ball Proximal Point Method converges to global minima in non-convex settings, generalizing quasiconvexity, star convexity, and related frameworks.
Neural decompositionality is defined via decision-boundary semantic preservation, and language transformers largely satisfy it under SAVED while vision models often do not.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Hybrid neural-process model derives biokinetic parameters from genomic traits for soil organic matter turnover, with ecological constraints, and outperforms baselines on synthetic and real data.
Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.
Tabular foundation models applied to PHM via signal-to-table conversion achieve the best average ranks across prognostic and diagnostic tasks and remain competitive in low-data regimes.
A parameter-efficient dual-encoder model with differentiable Choquet integral fusion improves underwater acoustic classification accuracy over single-encoder baselines on DeepShip and ShipsEar datasets.
Observation of ultrafast large dynamic magnetic soliton formation inside the linear spin-wave band in garnet films, extending tens of microns and collapsing into short-wavelength spin waves at large distances.
Models composed from bilinear factor, exponential link, Gamma prior, Gaussian likelihood, and equality node admit closed-form variational message passing under mean-field factorization.
Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
Equivariant neural networks for 2D Q-tensor prediction in nematic liquid crystals achieve lower errors and better generalization than non-equivariant models while satisfying symmetry constraints.
Symmetrizing cross-entropy produces the unique convex multi-class unhinged loss, which locally approximates other symmetric losses, and enables new interpolating losses SGCE and alpha-MAE with competitive performance on noisy-label benchmarks.
citing papers explorer
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Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
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Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations
CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
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Toy Combinatorial Interpretability Models Reveal Lottery Tickets in Early Feature Space
In a combinatorial toy setting, winning lottery tickets preserve families of compatible feature locations in early feature space that balance proximity to final codes with low interference, rather than specific weight subnetworks.
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DualTCN: A Physics-Constrained Temporal Convolutional Network for 2 Time-Domain Marine CSEM Inversion
DualTCN is the first deep-learning model for time-domain marine CSEM inversion that regresses four earth parameters, achieves high accuracy on simulated data, and runs up to 21,000 times faster than classical optimizers.
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Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Hybrid neural-process model derives biokinetic parameters from genomic traits for soil organic matter turnover, with ecological constraints, and outperforms baselines on synthetic and real data.
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A Geometric Measure of Linear Separability for Neural Representations
Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.
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Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models
Tabular foundation models applied to PHM via signal-to-table conversion achieve the best average ranks across prognostic and diagnostic tasks and remain competitive in low-data regimes.
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Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
Models composed from bilinear factor, exponential link, Gamma prior, Gaussian likelihood, and equality node admit closed-form variational message passing under mean-field factorization.
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Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels
Symmetrizing cross-entropy produces the unique convex multi-class unhinged loss, which locally approximates other symmetric losses, and enables new interpolating losses SGCE and alpha-MAE with competitive performance on noisy-label benchmarks.
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Extraction of linearized models from pre-trained networks via knowledge distillation
Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
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Neural Networks With Dense Weights Are Not Universal Approximators
Dense ReLU networks under natural weight and dimension constraints fail to approximate certain Lipschitz functions, unlike unrestricted networks.
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Fusion or Confusion? Multimodal Complexity Is Not All You Need
Complex multimodal architectures do not reliably outperform unimodal baselines or a simple multimodal baseline under standardized evaluation.
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HOLE: Homological Observation of Latent Embeddings for Neural Network Interpretability
HOLE applies persistent homology to latent embeddings in neural networks and uses visualizations such as cluster flow diagrams to reveal patterns of class separation, feature disentanglement, and robustness.
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Pulse Shape Discrimination Algorithms: Survey and Benchmark
A survey and benchmark of ~60 PSD algorithms on two radiation datasets finds deep learning models (MLPs and hybrids) often outperform traditional statistical methods, with an open-source Python/MATLAB toolbox and datasets released.
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SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
SEAGAN applies a domain-specific graph attention network to classify limitation states in A-Ci curves, achieving F1-score 0.857 and accuracy 0.882 on synthetic data with known ground truth.
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Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
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Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
AWA patterns from PD pulse amplitude, width, and area enable CNNs to classify single and mixed partial discharge sources under switching voltage with over 96% test accuracy.
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Foundation Models for Credit Risk Prediction: A Game Changer?
Tabular foundation models outperform standard methods in credit risk PD and LGD tasks, with larger gains on smaller datasets when used out-of-the-box.
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Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression
VCON is a unified framework for smooth iterative DNN compression that uses parallel execution and an affine combination to progressively replace the original model with its compressed form during fine-tuning.
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Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction
Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.
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Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.
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Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models
A 354-parameter shallow-deep neural network using age, AST, ALT, platelets and FIB-4 achieved external ROC-AUCs of 0.77 and 0.67 for advanced MASLD fibrosis, slightly above FIB-4's 0.75 and 0.60 on Malaysian and Indian cohorts.
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The Mathematics of AI Winters: The mathematical Taxonomy of Paradigm Fragility in AI Winter
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
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A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
A simulation-driven digital twin framework is shown to generate interpretable diabetes trajectories for decision-aware analysis by combining benchmark data with controlled synthetic scenarios.
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Supplementary Materials to Graph Convolutional Branch and Bound
Supplementary results on 1-tree relaxation performance inside a GCN-augmented branch-and-bound solver for TSP.
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Deep Learning in the Automotive Industry: Recent Advances and Application Examples
An overview of deep learning applications and challenges in the automotive industry, covering ADAS, automated driving, virtual sensing, and data-driven development.