A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
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13 Pith papers cite this work. Polarity classification is still indexing.
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LLMs process negation using both attention-based suppression and constructive representation mechanisms (construction dominant), with late-layer attention shortcuts explaining poor accuracy on negation tasks.
A Deep Set encoder plus normalizing flow model trained on five million CRPropa 3 events recovers UHECR source parameters without bias and classifies primary composition at over 98 percent accuracy.
ResRL decouples shared semantics between positive and negative responses in LLM reinforcement learning via SVD-based projection residuals, outperforming baselines including NSR by up to 9.4% on math reasoning benchmarks.
SDM sparsifies the Gated DeltaNet update rule to enable 1000x larger recurrent memory states at iso-FLOP, improving long-context recall and short-context reasoning over GDN and matching full attention at 8B scale.
The work augments pose-conditioned 3D Gaussian avatars with a residual latent evolved by a transformer decoder that decomposes updates into driving, restoring, and dissipative forces to produce history-dependent, temporally coherent full-body animations.
STELLAR trains up to 500M-parameter multi-modal models on 50M driving scenes and reports empirical scaling trends plus new state-of-the-art results on the Waymo Open Dataset.
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.
The paper casts the standard Transformer block with RoPE as a first-order approximation of a radial–tangential state estimator and introduces a Polar Transformer variant that retains the discarded geometric corrections.
mlr3torch introduces an extensible deep learning framework in R that integrates torch models into the mlr3 ecosystem via graph-based architectures for classification, regression, and multimodal tasks.
CMRU restores gradient flow in BMRU via cumulative state updates with skip-connections through time, yielding better convergence and benchmark performance while retaining quantized persistent memory.
Tuned classic GNNs outperform specialized multi-label node classification methods on four of five benchmarks and reach state-of-the-art in multiple settings.
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Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.