An acoustic dimer of two subwavelength scatterers can achieve unidirectional transverse scattering (transverse Kerker effect) while maintaining strong overall scattering.
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Attention is all you need.Advances in Neural Information Processing Systems
14 Pith papers cite this work. Polarity classification is still indexing.
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Dreamer 4 is the first agent to obtain diamonds in Minecraft from only offline data by reinforcement learning inside a scalable world model that accurately predicts game mechanics.
ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.
Crys-JEPA introduces a joint embedding predictive architecture that creates an energy-aware latent space, enabling embedding-based stability screening and a refinement pipeline that yields up to 72.7% gains on the V.S.U.N. metric for crystal generation.
Time-expanded interaction graphs with graph neural networks enable real-time prediction of surgical procedure duration deviations from team communication patterns and support counterfactual identification of beneficial changes.
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
DeMa is a dual-path delay-aware Mamba architecture that decomposes MTS into intra-series temporal and inter-series variate paths to achieve SOTA performance with linear complexity on forecasting, imputation, anomaly detection, and classification.
DAO pretrains Siamese diffusion-based models on stable/unstable crystal data to achieve 100% experimental match on Cr6Os2 and 2000x speedup over DFT on real superconductors.
VQ-VAE concept learning enables controllable recombination of crystal motifs to generate structures with reported gains in validity-stability-uniqueness-novelty metrics on MP-20 and Alex-MP-20.
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
AM-KNet improves position/velocity tracking error on two automotive datasets compared with base KalmanNet, but extent and covariance-consistency gains are inconsistent and partly misreported.
A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.
GLSTaGAT is a spatial-temporal graph attention network using data-driven fusion graphs, global-local blocks, node normalization, and a transformer encoder to outperform baselines on real-world network traffic datasets.
citing papers explorer
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FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing
An acoustic dimer of two subwavelength scatterers can achieve unidirectional transverse scattering (transverse Kerker effect) while maintaining strong overall scattering.
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Training Agents Inside of Scalable World Models
Dreamer 4 is the first agent to obtain diamonds in Minecraft from only offline data by reinforcement learning inside a scalable world model that accurately predicts game mechanics.
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Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation
ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.
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Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
Crys-JEPA introduces a joint embedding predictive architecture that creates an energy-aware latent space, enabling embedding-based stability screening and a refinement pipeline that yields up to 72.7% gains on the V.S.U.N. metric for crystal generation.
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Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
Time-expanded interaction graphs with graph neural networks enable real-time prediction of surgical procedure duration deviations from team communication patterns and support counterfactual identification of beneficial changes.
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GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
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DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
DeMa is a dual-path delay-aware Mamba architecture that decomposes MTS into intra-series temporal and inter-series variate paths to achieve SOTA performance with linear complexity on forecasting, imputation, anomaly detection, and classification.
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Siamese Foundation Models for Crystal Structure Prediction
DAO pretrains Siamese diffusion-based models on stable/unstable crystal data to achieve 100% experimental match on Cr6Os2 and 2000x speedup over DFT on real superconductors.
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Composable Crystals: Controllable Materials Discovery via Concept Learning
VQ-VAE concept learning enables controllable recombination of crystal motifs to generate structures with reported gains in validity-stability-uniqueness-novelty metrics on MP-20 and Alex-MP-20.
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Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
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An Explainable Approach to Document-level Translation Evaluation with Topic Modeling
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
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Adaptive Learned State Estimation based on KalmanNet
AM-KNet improves position/velocity tracking error on two automotive datasets compared with base KalmanNet, but extent and covariance-consistency gains are inconsistent and partly misreported.
-
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.
-
Global-local Spatial-temporal Aware Graph Attention Network for Network Traffic Forecasting
GLSTaGAT is a spatial-temporal graph attention network using data-driven fusion graphs, global-local blocks, node normalization, and a transformer encoder to outperform baselines on real-world network traffic datasets.