MinMax RNCs are recurrent neural models using min-max recurrence that achieve full regular-language expressivity, logarithmic parallel evaluation, uniformly bounded states, and constant state gradients independent of time distance.
citation dossier
Layer Normalization
Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.
164Pith papers citing it
173reference links
cs.LGtop field · 63 papers
UNVERDICTEDtop verdict bucket · 140 papers
why this work matters in Pith
Pith has found this work in 164 reviewed papers. Its strongest current cluster is cs.LG (63 papers). The largest review-status bucket among citing papers is UNVERDICTED (140 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
Local attention strictly enlarges the class of regular languages recognizable by fixed-precision transformers by adding a second past operator in linear temporal logic, with global and local attention being expressively complementary.
Mamba is a linear-time sequence model using input-dependent selective SSMs that achieves SOTA results across modalities and matches twice-larger Transformers on language modeling with 5x higher inference throughput.
The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
Reformer matches standard Transformer accuracy on long sequences while using far less memory and running faster via LSH attention and reversible residual layers.
A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
Temporarily reducing the learning rate on upper-layer query and key projections during early GPT pretraining prevents premature attention specialization and improves model performance.
OpenSGA fuses vision-language, textual, and geometric features via a distance-gated attention encoder and minimum-cost-flow allocator to outperform prior methods on both frame-to-scan and subscan-to-subscan 3D scene graph alignment, backed by a new 700k-sample ScanNet-SG dataset.
MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
Aligned LLMs exhibit Refusal-Escape Directions (RED) that enable refusal-to-answer transitions via input perturbations; these directions decompose exactly into operator-level sources, creating an inherent safety-utility trade-off when trying to eliminate them.
Delta-Adapter extracts a semantic delta from a single image pair via a pre-trained vision encoder and injects it through a Perceiver adapter to enable scalable single-pair supervised editing.
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
QuadNorm uses quadrature-based moments instead of uniform averaging in normalization layers, achieving O(h²) consistency across resolutions and better cross-resolution transfer in neural operators.
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
A Max-Cut-specific graph neural network predicts primal- and dual-feasible SDP solutions in linearithmic time, cutting bounding costs in exact branch-and-bound by up to 10.6 times versus a commercial SDP solver while training without any solved SDP labels.
Every fixed finite feedforward neural network definable in an o-minimal structure has finite sample complexity in the agnostic PAC setting.
SOPE uses an actor-aligned OPE signal on a held-out validation split to dynamically stop offline stabilization phases in online RL, improving performance up to 45.6% and cutting TFLOPs up to 22x on 25 Minari tasks.
In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
PHALAR achieves up to 70% relative accuracy gain in stem retrieval with under half the parameters and 7x faster training by using phasor-based equivariant representations, setting new SOTA on multiple datasets.
A fully differentiable TensorFlow gyrokinetic code allows approximate gradients of nonlinear turbulence quantities to be used for outer-loop tasks such as profile prediction despite stochasticity.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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.
DEFault++ delivers automated hierarchical fault detection, categorization into 12 transformer-specific types, and root-cause diagnosis among 45 mechanisms on a new benchmark of 3,739 mutated instances, with AUROC >0.96 and Macro-F1 0.85, plus improved developer repair accuracy in a user study.
citing papers explorer
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MinMax Recurrent Neural Cascades
MinMax RNCs are recurrent neural models using min-max recurrence that achieve full regular-language expressivity, logarithmic parallel evaluation, uniformly bounded states, and constant state gradients independent of time distance.
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Characterizing the Expressivity of Local Attention in Transformers
Local attention strictly enlarges the class of regular languages recognizable by fixed-precision transformers by adding a second past operator in linear temporal logic, with global and local attention being expressively complementary.
-
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Mamba is a linear-time sequence model using input-dependent selective SSMs that achieves SOTA results across modalities and matches twice-larger Transformers on language modeling with 5x higher inference throughput.
-
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
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Reformer: The Efficient Transformer
Reformer matches standard Transformer accuracy on long sequences while using far less memory and running faster via LSH attention and reversible residual layers.
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Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
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Learning Less Is More: Premature Upper-Layer Attention Specialization Hurts Language Model Pretraining
Temporarily reducing the learning rate on upper-layer query and key projections during early GPT pretraining prevents premature attention specialization and improves model performance.
-
OpenSGA: Efficient 3D Scene Graph Alignment in the Open World
OpenSGA fuses vision-language, textual, and geometric features via a distance-gated attention encoder and minimum-cost-flow allocator to outperform prior methods on both frame-to-scan and subscan-to-subscan 3D scene graph alignment, backed by a new 700k-sample ScanNet-SG dataset.
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Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
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Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-off
Aligned LLMs exhibit Refusal-Escape Directions (RED) that enable refusal-to-answer transitions via input perturbations; these directions decompose exactly into operator-level sources, creating an inherent safety-utility trade-off when trying to eliminate them.
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Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision
Delta-Adapter extracts a semantic delta from a single image pair via a pre-trained vision encoder and injects it through a Perceiver adapter to enable scalable single-pair supervised editing.
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Neural network quantum states in the grand canonical ensemble
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
-
QuadNorm: Resolution-Robust Normalization for Neural Operators
QuadNorm uses quadrature-based moments instead of uniform averaging in normalization layers, achieving O(h²) consistency across resolutions and better cross-resolution transfer in neural operators.
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GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
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Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
A Max-Cut-specific graph neural network predicts primal- and dual-feasible SDP solutions in linearithmic time, cutting bounding costs in exact branch-and-bound by up to 10.6 times versus a commercial SDP solver while training without any solved SDP labels.
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Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
Every fixed finite feedforward neural network definable in an o-minimal structure has finite sample complexity in the agnostic PAC setting.
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SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
SOPE uses an actor-aligned OPE signal on a held-out validation split to dynamically stop offline stabilization phases in online RL, improving performance up to 45.6% and cutting TFLOPs up to 22x on 25 Minari tasks.
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How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences
In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
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PHALAR: Phasors for Learned Musical Audio Representations
PHALAR achieves up to 70% relative accuracy gain in stem retrieval with under half the parameters and 7x faster training by using phasor-based equivariant representations, setting new SOTA on multiple datasets.
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iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow
A fully differentiable TensorFlow gyrokinetic code allows approximate gradients of nonlinear turbulence quantities to be used for outer-loop tasks such as profile prediction despite stochasticity.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
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.
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DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures
DEFault++ delivers automated hierarchical fault detection, categorization into 12 transformer-specific types, and root-cause diagnosis among 45 mechanisms on a new benchmark of 3,739 mutated instances, with AUROC >0.96 and Macro-F1 0.85, plus improved developer repair accuracy in a user study.
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Learning Neural Operator Surrogates for the Black Hole Accretion Code
Physics-informed Fourier neural operators recover plasmoid formation in sparse SRRMHD vortex data where data-only models fail, and transformer operators approximate AMR jet evolution, marking first reported uses in these relativistic MHD settings.
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Pareto Frontier of Neural Quantum States: Scalable, Affordable, and Accurate Convolutional Backflow for Strongly Correlated Lattice Fermions
SCALE and ACE are new convolutional backflow architectures for Neural Quantum States that deliver O(N^3) scaling with high accuracy and over 40x speedup on Hubbard and t-J models up to 32x32 lattices.
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Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
Reference-augmented learning with RNN surrogate and stochastic perturbations cuts average position error by 50.9% for 6-DOF tracking on a three-section TDCR compared to non-augmented baselines.
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HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA
HAC provides a parameter-efficient way to move CLIP into hyperbolic geometry, yielding consistent gains on zero-shot VQA benchmarks without any VQA training data overlap.
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A satellite foundation model for improved wealth monitoring
Tempov is a self-supervised satellite foundation model that predicts wealth levels and decadal changes at high resolution across Africa from Landsat imagery, outperforming baselines even with limited labels and generalizing temporally.
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Latent Space Probing for Adult Content Detection in Video Generative Models
Latent space probing on CogVideoX achieves 97.29% F1 for adult content detection on a new 11k-clip dataset with 4-6ms overhead.
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To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning
Unembedding collapse in transformers prevents distinguishing unseen tokens in symbolic reasoning, but targeted interventions restore generalization.
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Linear Image Generation by Synthesizing Exposure Brackets
The paper introduces a DiT-based flow-matching model that generates linear images by synthesizing text-conditioned exposure brackets to preserve full dynamic range.
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Understanding and Enforcing Weight Disentanglement in Task Arithmetic
Task-Feature Specialization explains weight disentanglement in task arithmetic and leads to orthogonality, which OrthoReg enforces to enhance performance of model composition methods.
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Machine learning isotope shifts in molecular energy levels
Neural network corrects residual errors in isotopologue energy extrapolations for CO2 (MAE reduction in >87% of levels vs Marvel) and transfers patterns to improve CO predictions in >93% of samples.
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DEMUX: Boundary-Aware Multi-Scale Traffic Demixing for Multi-Tab Website Fingerprinting
DEMUX achieves state-of-the-art multi-tab website fingerprinting accuracy by preserving boundary signals, modeling at multiple scales, and associating dispersed traffic fragments with a new three-component architecture.
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Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
SIF encodes full historical raw samples as tokens via hierarchical quantization to preserve sample context and unify sequential/non-sequential features in large recommender models.
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Data-driven oscillator model for multi-frequency turbulent flows
A data-driven framework extracts oscillators from multi-frequency turbulent flow data via autoencoders and models their dynamics with neural networks to enable long-term forecasting, demonstrated on supersonic cavity flow.
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One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
Scale-autoregressive modeling (SAR) samples fluid flow distributions hierarchically from coarse to fine resolutions on meshes, achieving lower distributional error and 2-7x faster runtime than diffusion or flow-matching baselines.
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Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation
The first survey on Attention Sink in Transformers structures the literature around fundamental utilization, mechanistic interpretation, and strategic mitigation.
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Envisioning the Future, One Step at a Time
An autoregressive diffusion model on sparse point trajectories predicts multi-modal future scene dynamics from single images with orders-of-magnitude faster sampling than dense video simulators while matching accuracy.
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WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
WOMBET generates reliable prior data with world-model uncertainty penalization and transfers it to target tasks via adaptive offline-online sampling, yielding better sample efficiency than baselines.
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Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings
Dual Triangle Attention achieves effective bidirectional attention with built-in positional inductive bias via dual triangular masks, outperforming standard bidirectional attention on position-sensitive tasks and showing strong masked language modeling results with or without positional embeddings.
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Multi-Head Attention based interaction-aware architecture for Bangla Handwritten Character Recognition: Introducing a Primary Dataset
A new balanced Bangla handwritten character dataset paired with a multi-head attention hybrid model using EfficientNetB3, ViT, and Conformer achieves high accuracy and strong generalization.
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Unified Vector Floorplan Generation via Markup Representation
A single transformer model using a new markup representation generates functional floorplans from diverse conditions and outperforms prior task-specific methods on the RPLAN dataset.
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Deformation-based In-Context Learning for Point Cloud Understanding
DeformPIC deforms query point clouds under prompt guidance for in-context learning, outperforming prior methods with lower Chamfer Distance on reconstruction, denoising, and registration tasks.
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Stochastic Policy Gradient Methods in the Uncertain Volatility Model
A neural-network actor-critic policy gradient algorithm with squashed Gaussian C-vine policies solves high-dimensional robust pricing problems in the uncertain volatility model and outperforms existing Monte Carlo and ML benchmarks in numerical tests.
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Efficient Memory Management for Large Language Model Serving with PagedAttention
PagedAttention achieves near-zero waste in LLM key-value cache memory and enables 2-4x higher serving throughput than prior systems.
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Segment Anything
A promptable model trained on 1B masks achieves competitive zero-shot segmentation performance across tasks and is released publicly with its dataset.
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High Fidelity Neural Audio Compression
EnCodec is an end-to-end trained streaming neural audio codec that uses a single multiscale spectrogram discriminator and a gradient-normalizing loss balancer to achieve higher fidelity than prior methods at the same bitrates for 24 kHz mono and 48 kHz stereo audio.
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DreamFusion: Text-to-3D using 2D Diffusion
Optimizes a Neural Radiance Field via probability density distillation from a 2D diffusion model to produce text-conditioned 3D scenes viewable from any angle.