{"total":43,"rows":[{"id":"2105.02375","title":"A Geometric Analysis of Neural Collapse with Unconstrained Features","created":"2021-05-06","cited_by_count":227,"area":null},{"id":"2203.09081","title":"Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?","created":"2022-03-17","cited_by_count":100,"area":null},{"id":"2203.16588","title":"Constrained Few-shot Class-incremental Learning","created":"2022-03-30","cited_by_count":94,"area":null},{"id":"2102.12470","title":"On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)","created":"2021-02-24","cited_by_count":73,"area":null},{"id":"2410.01131","title":"nGPT: Normalized Transformer with Representation Learning on the Hypersphere","created":"2024-10-01","cited_by_count":70,"area":null},{"id":"2102.08817","title":"Dissecting Supervised Contrastive Learning","created":"2021-02-17","cited_by_count":64,"area":null},{"id":"2110.12899","title":"No One Representation to Rule Them All: Overlapping Features of Training Methods","created":"2021-10-20","cited_by_count":58,"area":null},{"id":"2103.10994","title":"Self-Supervised Classification Network","created":"2021-03-19","cited_by_count":28,"area":null},{"id":"2103.15632","title":"Regular Polytope Networks","created":"2021-03-29","cited_by_count":24,"area":null},{"id":"2206.15369","title":"No Reason for No Supervision: Improved Generalization in Supervised Models","created":"2022-06-30","cited_by_count":23,"area":null},{"id":"2106.15739","title":"On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay","created":"2021-06-29","cited_by_count":20,"area":null},{"id":"2207.09413","title":"SphereFed: Hyperspherical Federated Learning","created":"2022-07-19","cited_by_count":19,"area":null},{"id":"2111.07632","title":"CoReS: Compatible Representations via Stationarity","created":"2021-11-15","cited_by_count":14,"area":null},{"id":"2308.01746","title":"Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants","created":"2023-08-03","cited_by_count":14,"area":null},{"id":"2411.01248","title":"Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame","created":"2024-11-02","cited_by_count":14,"area":null},{"id":"2303.05689","title":"Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severity","created":"2023-03-10","cited_by_count":13,"area":null},{"id":"2106.09821","title":"Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing","created":"2021-06-17","cited_by_count":12,"area":null},{"id":"2205.08099","title":"Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey","created":"2022-05-17","cited_by_count":12,"area":null},{"id":"2401.09067","title":"Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding","created":"2024-01-17","cited_by_count":10,"area":null},{"id":"2310.00893","title":"Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss","created":"2023-10-02","cited_by_count":8,"area":null},{"id":"2310.07996","title":"Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning","created":"2023-10-12","cited_by_count":6,"area":null},{"id":"2101.06475","title":"Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks","created":"2021-01-16","cited_by_count":5,"area":null},{"id":"2106.04767","title":"Ex uno plures: Splitting One Model into an Ensemble of Subnetworks","created":"2021-06-09","cited_by_count":5,"area":null},{"id":"2301.06116","title":"Maximally Compact and Separated Features with Regular Polytope Networks","created":"2023-01-15","cited_by_count":5,"area":null},{"id":"2303.13957","title":"Factorizers for Distributed Sparse Block Codes","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2310.09449","title":"Pairwise Similarity Learning is SimPLE","created":"2023-10-13","cited_by_count":4,"area":null},{"id":"2412.16884","title":"Out-of-Distribution Detection with Prototypical Outlier Proxy","created":"2024-12-22","cited_by_count":4,"area":null},{"id":"2403.16937","title":"Hyperspherical Classification with Dynamic Label-to-Prototype Assignment","created":"2024-03-25","cited_by_count":3,"area":null},{"id":"2411.03033","title":"Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2111.01742","title":"LogAvgExp Provides a Principled and Performant Global Pooling Operator","created":"2021-11-02","cited_by_count":2,"area":null},{"id":"2210.03053","title":"Reinforcement Learning with Large Action Spaces for Neural Machine Translation","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2302.05334","title":"The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study","created":"2023-02-10","cited_by_count":2,"area":null},{"id":"2505.03012","title":"GIF: Generative Inspiration for Face Recognition at Scale","created":"2025-05-05","cited_by_count":2,"area":null},{"id":"2203.12928","title":"The Fixed Sub-Center: A Better Way to Capture Data Complexity","created":"2022-03-24","cited_by_count":1,"area":null},{"id":"2609.04381","title":"What Do Scan-Derived Class Prototypes Add? Disentangling Supervision, Prototype Content and Query Protocol in Recognition over Frozen Foundation Features","created":"2026-09-03","cited_by_count":1,"area":null},{"id":"2103.11112","title":"Classifier Crafting: Turn Your ConvNet into a Zero-Shot Learner!","created":"2021-03-20","cited_by_count":0,"area":null},{"id":"2601.01679","title":"Simplex Deep Linear Discriminant Analysis","created":"2026-01-04","cited_by_count":0,"area":null},{"id":"2605.18224","title":"RST: certifying and constructing prescribed information in variational autoencoders","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.09536","title":"Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2607.12916","title":"Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2607.18343","title":"Federated Lightweight Fine-Tuning","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.30003","title":"OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.35440","title":"SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}