{"total":134,"rows":[{"id":"2312.10794","title":"A mathematical perspective on Transformers","created":"2023-12-17","cited_by_count":164,"area":null},{"id":"2305.05465","title":"The emergence of clusters in self-attention dynamics","created":"2023-05-09","cited_by_count":140,"area":null},{"id":"2407.11046","title":"A Survey on LoRA of Large Language Models","created":"2024-07-08","cited_by_count":67,"area":null},{"id":"2312.14820","title":"How Smooth Is Attention?","created":"2023-12-22","cited_by_count":44,"area":null},{"id":"2408.01367","title":"Transformers are Universal In-context Learners","created":"2024-08-02","cited_by_count":43,"area":null},{"id":"2209.15466","title":"Sparsity-Constrained Optimal Transport","created":"2022-09-30","cited_by_count":32,"area":null},{"id":"2411.04990","title":"Clustering in Causal Attention Masking","created":"2024-11-07","cited_by_count":32,"area":null},{"id":"2512.01868","title":"The Mean-Field Dynamics of Transformers","created":"2025-12-01","cited_by_count":30,"area":null},{"id":"2411.04551","title":"Measure-to-measure interpolation using Transformers","created":"2024-11-07","cited_by_count":29,"area":null},{"id":"2205.14612","title":"Do Residual Neural Networks discretize Neural Ordinary Differential Equations?","created":"2022-05-29","cited_by_count":27,"area":null},{"id":"2301.01766","title":"Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow","created":"2023-01-04","cited_by_count":27,"area":null},{"id":"2305.05080","title":"Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey","created":"2023-05-08","cited_by_count":27,"area":null},{"id":"2504.14697","title":"Quantitative Clustering in Mean-Field Transformer Models","created":"2025-04-20","cited_by_count":26,"area":null},{"id":"2509.25040","title":"A multiscale analysis of mean-field transformers in the moderate interaction regime","created":"2025-09-29","cited_by_count":25,"area":null},{"id":"2310.06396","title":"Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach","created":"2023-10-10","cited_by_count":24,"area":null},{"id":"2306.05272","title":"Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models","created":"2023-06-08","cited_by_count":22,"area":null},{"id":"2401.04301","title":"Setting the Record Straight on Transformer Oversmoothing","created":"2024-01-09","cited_by_count":21,"area":null},{"id":"2309.01213","title":"Implicit regularization of deep residual networks towards neural ODEs","created":"2023-09-03","cited_by_count":20,"area":null},{"id":"2402.05787","title":"How do Transformers perform In-Context Autoregressive Learning?","created":"2024-02-08","cited_by_count":17,"area":null},{"id":"2405.18273","title":"Synchronization on circles and spheres with nonlinear interactions","created":"2024-05-28","cited_by_count":17,"area":null},{"id":"2301.13197","title":"Unlocking Slot Attention by Changing Optimal Transport Costs","created":"2023-01-30","cited_by_count":16,"area":null},{"id":"2407.01602","title":"Clustering in pure-attention hardmax transformers and its role in sentiment analysis","created":"2024-06-26","cited_by_count":15,"area":null},{"id":"2206.07630","title":"Rethinking Initialization of the Sinkhorn Algorithm","created":"2022-06-15","cited_by_count":14,"area":null},{"id":"2503.01329","title":"Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning","created":"2025-03-03","cited_by_count":13,"area":null},{"id":"2301.01805","title":"Unsupervised Manifold Linearizing and Clustering","created":"2023-01-04","cited_by_count":12,"area":null},{"id":"2305.13797","title":"SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities","created":"2023-05-23","cited_by_count":12,"area":null},{"id":"2507.22857","title":"Synchronization of mean-field models on the circle","created":"2025-07-30","cited_by_count":12,"area":null},{"id":"2210.05794","title":"Designing Robust Transformers using Robust Kernel Density Estimation","created":"2022-10-11","cited_by_count":11,"area":null},{"id":"2602.03067","title":"FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPU","created":"2026-02-03","cited_by_count":11,"area":null},{"id":"2303.04940","title":"Non-aligned supervision for Real Image Dehazing","created":"2023-03-08","cited_by_count":9,"area":null},{"id":"2403.14183","title":"OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation","created":"2024-03-21","cited_by_count":9,"area":null},{"id":"2410.03011","title":"Towards Understanding the Universality of Transformers for Next-Token Prediction","created":"2024-10-03","cited_by_count":9,"area":null},{"id":"2503.03025","title":"Hierarchical Refinement: Optimal Transport to Infinity and Beyond","created":"2025-03-04","cited_by_count":9,"area":null},{"id":"2510.22026","title":"Normalization in Attention Dynamics","created":"2025-10-24","cited_by_count":9,"area":null},{"id":"2310.16741","title":"Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets","created":"2023-10-25","cited_by_count":8,"area":null},{"id":"2505.13499","title":"Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency","created":"2025-05-16","cited_by_count":8,"area":null},{"id":"2511.11553","title":"Multistability of Self-Attention Dynamics in Transformers","created":"2025-11-14","cited_by_count":8,"area":null},{"id":"2601.21366","title":"Perceptrons and localization of attention's mean-field landscape","created":"2026-01-29","cited_by_count":8,"area":null},{"id":"2601.21942","title":"Clustering in Deep Stochastic Transformers","created":"2026-01-29","cited_by_count":8,"area":null},{"id":"2305.19727","title":"Unbalanced Low-rank Optimal Transport Solvers","created":"2023-05-31","cited_by_count":7,"area":null},{"id":"2406.13762","title":"Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2406.13770","title":"Elliptical Attention","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2501.18793","title":"OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization","created":"2025-01-30","cited_by_count":7,"area":null},{"id":"2505.16284","title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","created":"2025-05-22","cited_by_count":7,"area":null},{"id":"2306.06119","title":"Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction","created":"2023-06-05","cited_by_count":6,"area":null},{"id":"2504.16275","title":"Quantum Doubly Stochastic Transformers","created":"2025-04-22","cited_by_count":6,"area":null},{"id":"2509.25611","title":"Transformers through the lens of support-preserving maps between measures","created":"2025-09-30","cited_by_count":6,"area":null},{"id":"2512.06797","title":"Optimal and Diffusion Transports in Machine Learning","created":"2025-12-07","cited_by_count":6,"area":null},{"id":"2406.10823","title":"Iterated Schr\\\"odinger bridge approximation to Wasserstein Gradient Flows","created":"2024-06-16","cited_by_count":5,"area":null},{"id":"2507.02748","title":"Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics","created":"2025-07-03","cited_by_count":5,"area":null},{"id":"2605.10931","title":"Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime","created":"2026-05-11","cited_by_count":5,"area":null},{"id":"2404.09402","title":"Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes","created":"2024-04-15","cited_by_count":4,"area":null},{"id":"2404.14265","title":"Deep Learning as Ricci Flow","created":"2024-04-22","cited_by_count":4,"area":null},{"id":"2406.13781","title":"A Primal-Dual Framework for Transformers and Neural Networks","created":"2024-06-19","cited_by_count":4,"area":null},{"id":"2408.02932","title":"Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping","created":"2024-08-06","cited_by_count":4,"area":null},{"id":"2410.09795","title":"WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction","created":"2024-10-13","cited_by_count":4,"area":null},{"id":"2412.09080","title":"On the number of modes of Gaussian kernel density estimators","created":"2024-12-12","cited_by_count":4,"area":null},{"id":"2412.09235","title":"A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithm","created":"2024-12-12","cited_by_count":4,"area":null},{"id":"2506.15881","title":"T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2509.00421","title":"Memory Limitations of Prompt Tuning in Transformers","created":"2025-08-30","cited_by_count":4,"area":null},{"id":"2509.23436","title":"LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport","created":"2025-09-27","cited_by_count":4,"area":null},{"id":"2603.06187","title":"Random Quadratic Form on a Sphere: Synchronization by Common Noise","created":"2026-03-06","cited_by_count":4,"area":null},{"id":"2401.04372","title":"Stable generative modeling using Schr\\\"odinger bridges","created":"2024-01-09","cited_by_count":3,"area":null},{"id":"2402.14991","title":"Quantum Theory and Application of Contextual Optimal Transport","created":"2024-02-22","cited_by_count":3,"area":null},{"id":"2409.07968","title":"Localized Schr\\\"odinger Bridge Sampler","created":"2024-09-12","cited_by_count":3,"area":null},{"id":"2412.18288","title":"Towards understanding how attention mechanism works in deep learning","created":"2024-12-24","cited_by_count":3,"area":null},{"id":"2503.00687","title":"Transformer Meets Twicing: Harnessing Unattended Residual Information","created":"2025-03-02","cited_by_count":3,"area":null},{"id":"2507.20453","title":"Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations","created":"2025-07-28","cited_by_count":3,"area":null},{"id":"2512.10656","title":"Token Sample Complexity of Attention","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2512.11784","title":"Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective","created":"2025-12-12","cited_by_count":3,"area":null},{"id":"2605.16747","title":"Propagation of Chaos in Contextual Flow Maps","created":"2026-05-16","cited_by_count":3,"area":null},{"id":"2206.00206","title":"Transformer with Fourier Integral Attentions","created":"2022-06-01","cited_by_count":2,"area":null},{"id":"2212.06339","title":"Regularized Optimal Transport Layers for Generalized Global Pooling Operations","created":"2022-12-13","cited_by_count":2,"area":null},{"id":"2407.01467","title":"The Balanced-Pairwise-Affinities Feature Transform","created":"2024-06-25","cited_by_count":2,"area":null},{"id":"2407.04170","title":"Attention Normalization Impacts Cardinality Generalization in Slot Attention","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2410.10914","title":"Towards Better Multi-head Attention via Channel-wise Sample Permutation","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2502.11646","title":"Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2509.15958","title":"Localmax dynamics for attention in transformers and its asymptotic behavior","created":"2025-09-19","cited_by_count":2,"area":null},{"id":"2512.11411","title":"Sliced ReLU attention: Quasi-linear contextual expressivity via sorting","created":"2025-12-12","cited_by_count":2,"area":null},{"id":"2601.23236","title":"YuriiFormer: A Suite of Nesterov-Accelerated Transformers","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2602.08216","title":"Entropy-Generated Attention Beyond Softmax and Entmax: Kaniadakis and Reciprocal-Symmetric Abe Operators","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2605.23778","title":"The physics of AI weather models","created":"2026-05-22","cited_by_count":2,"area":null},{"id":"2605.28075","title":"Measure-to-measure Regression with Transformers","created":"2026-05-27","cited_by_count":2,"area":null},{"id":"2302.14346","title":"Sampled Transformer for Point Sets","created":"2023-02-28","cited_by_count":1,"area":null},{"id":"2310.17683","title":"Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks","created":"2023-10-26","cited_by_count":1,"area":null},{"id":"2410.19931","title":"Provable optimal transport with transformers: The essence of depth and prompt engineering","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2509.01685","title":"Preconditioned Regularized Wasserstein Proximal Sampling","created":"2025-09-01","cited_by_count":1,"area":null},{"id":"2510.16356","title":"Sparse Transformer Architectures via Regularized Wasserstein Proximal Operator with $L_1$ Prior","created":"2025-10-18","cited_by_count":1,"area":null},{"id":"2601.13953","title":"Differentiable Logic Synthesis: Spectral Coefficient Selection via Sinkhorn-Constrained Composition","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2603.16535","title":"SympFormer: Accelerated attention blocks via Inertial Dynamics on Density Manifolds","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2603.25860","title":"On the Expressive Power of Transformers for Contextual Relations","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2605.07772","title":"Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.17660","title":"Training Infinitely Deep and Wide Transformers","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2605.18870","title":"Multi-Headed Transformer Architectures as Time-dependent Wasserstein Gradient Flows","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2605.21365","title":"$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.05140","title":"Phase transitions for the noisy transformer model in arbitrary dimension","created":"2026-06-03","cited_by_count":1,"area":null},{"id":"2606.07600","title":"Reachability and asymptotics of Gaussian Transformer dynamics","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2608.10288","title":"Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2609.02672","title":"oHC: Orthogonal Hyper-Connections on SO(4) via Quaternions","created":"2026-09-02","cited_by_count":1,"area":null},{"id":"2609.35483","title":"Universal Approximation of Measure-to-Measure Operators by Pushforwards","created":"2026-09-28","cited_by_count":1,"area":null}],"next_cursor":"100"}