{"list":"other","total":82,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"1506.02626","title":"Learning both Weights and Connections for Efficient Neural Networks","created":"2015-06-08","cited_by_count":2031},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1602.07360","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","created":"2016-02-24","cited_by_count":1060},{"id":"2403.14608","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","created":"2024-03-21","cited_by_count":656},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1506.02557","title":"Variational Dropout and the Local Reparameterization Trick","created":"2015-06-08","cited_by_count":490},{"id":"1808.01974","title":"A Survey on Deep Transfer Learning","created":"2018-08-06","cited_by_count":419},{"id":"2007.12223","title":"The Lottery Ticket Hypothesis for Pre-trained BERT Networks","created":"2020-07-23","cited_by_count":275},{"id":"2012.13490","title":"Towards Continual Reinforcement Learning: A Review and Perspectives","created":"2020-12-25","cited_by_count":274},{"id":"2411.02337","title":"WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning","created":"2024-11-04","cited_by_count":256},{"id":"1711.05908","title":"NISP: Pruning Networks using Neuron Importance Score Propagation","created":"2017-11-16","cited_by_count":228},{"id":"1411.4280","title":"Efficient Object Localization Using Convolutional Networks","created":"2014-11-16","cited_by_count":227},{"id":"1705.07832","title":"Concrete Dropout","created":"2017-05-22","cited_by_count":227},{"id":"1602.03409","title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","created":"2016-02-10","cited_by_count":209},{"id":"1802.02871","title":"Online Learning: A Comprehensive Survey","created":"2018-02-08","cited_by_count":205},{"id":"2106.14448","title":"R-Drop: Regularized Dropout for Neural Networks","created":"2021-06-28","cited_by_count":202},{"id":"1302.4389","title":"Maxout Networks","created":"2013-02-18","cited_by_count":187},{"id":"1805.12369","title":"Reinforced Continual Learning","created":"2018-05-31","cited_by_count":186},{"id":"1803.01442","title":"Stochastic Activation Pruning for Robust Adversarial Defense","created":"2018-03-05","cited_by_count":171},{"id":"1811.08737","title":"SpotTune: Transfer Learning through Adaptive Fine-tuning","created":"2018-11-21","cited_by_count":168},{"id":"1611.01353","title":"Information Dropout: Learning Optimal Representations Through Noisy Computation","created":"2016-11-04","cited_by_count":132},{"id":"2010.07611","title":"Layer-adaptive sparsity for the Magnitude-based Pruning","created":"2020-10-15","cited_by_count":126},{"id":"1812.11806","title":"An introduction to domain adaptation and transfer learning","created":"2018-12-31","cited_by_count":112},{"id":"2307.11046","title":"A Definition of Continual Reinforcement Learning","created":"2023-07-20","cited_by_count":101},{"id":"2401.02415","title":"LLaMA Pro: Progressive LLaMA with Block Expansion","created":"2024-01-04","cited_by_count":101},{"id":"1307.1493","title":"Dropout Training as Adaptive Regularization","created":"2013-07-04","cited_by_count":93},{"id":"2107.04384","title":"Continual Learning in the Teacher-Student Setup: Impact of Task Similarity","created":"2021-07-09","cited_by_count":88},{"id":"1711.03705","title":"Online Deep Learning: Learning Deep Neural Networks on the Fly","created":"2017-11-10","cited_by_count":86},{"id":"2403.05175","title":"Continual Learning and Catastrophic Forgetting","created":"2024-03-08","cited_by_count":77},{"id":"2102.09517","title":"Essentials for Class Incremental Learning","created":"2021-02-18","cited_by_count":57},{"id":"2204.08817","title":"An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions","created":"2022-04-19","cited_by_count":54},{"id":"1703.06229","title":"Curriculum Dropout","created":"2017-03-18","cited_by_count":50},{"id":"1611.06440","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","created":"2016-11-19","cited_by_count":48},{"id":"1907.02649","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","created":"2019-07-05","cited_by_count":38},{"id":"2303.14151","title":"Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle","created":"2023-03-24","cited_by_count":37},{"id":"1904.03292","title":"The Information Complexity of Learning Tasks, their Structure and their Distance","created":"2019-04-05","cited_by_count":36},{"id":"2009.11839","title":"A Gradient Flow Framework For Analyzing Network Pruning","created":"2020-09-24","cited_by_count":36},{"id":"1511.06066","title":"Transfer Learning for Speech and Language Processing","created":"2015-11-19","cited_by_count":35},{"id":"2404.09610","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","created":"2024-04-15","cited_by_count":35},{"id":"2007.04239","title":"A Survey on Transfer Learning in Natural Language Processing","created":"2020-05-31","cited_by_count":33},{"id":"2207.10888","title":"FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification","created":"2022-07-22","cited_by_count":30},{"id":"2207.01382","title":"Exploring Lottery Ticket Hypothesis in Spiking Neural Networks","created":"2022-07-04","cited_by_count":26},{"id":"2308.04268","title":"Teacher-Student Architecture for Knowledge Distillation: A Survey","created":"2023-08-08","cited_by_count":26},{"id":"2403.04861","title":"A Survey of Lottery Ticket Hypothesis","created":"2024-03-07","cited_by_count":24},{"id":"1603.05118","title":"Recurrent Dropout without Memory Loss","created":"2016-03-16","cited_by_count":22},{"id":"1512.05463","title":"Continuous online sequence learning with an unsupervised neural network model","created":"2015-12-17","cited_by_count":18},{"id":"2107.04689","title":"Lifelong Teacher-Student Network Learning","created":"2021-07-09","cited_by_count":18},{"id":"2208.06931","title":"A Theory for Knowledge Transfer in Continual Learning","created":"2022-08-14","cited_by_count":16},{"id":"2210.04643","title":"Critical Learning Periods for Multisensory Integration in Deep Networks","created":"2022-10-06","cited_by_count":14},{"id":"2407.13044","title":"DropKAN: Regularizing KANs by masking post-activations","created":"2024-07-17","cited_by_count":14},{"id":"1611.06791","title":"Generalized Dropout","created":"2016-11-21","cited_by_count":13},{"id":"2209.14624","title":"Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning","created":"2022-09-29","cited_by_count":13},{"id":"2307.10698","title":"Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data","created":"2023-07-20","cited_by_count":13},{"id":"2406.10275","title":"ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets","created":"2024-06-11","cited_by_count":11},{"id":"2304.12214","title":"Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration","created":"2023-04-24","cited_by_count":10},{"id":"2309.08044","title":"How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent","created":"2023-09-14","cited_by_count":10},{"id":"2412.16834","title":"Online Learning from Strategic Human Feedback in LLM Fine-Tuning","created":"2024-12-22","cited_by_count":10},{"id":"2202.00774","title":"Accelerating DNN Training with Structured Data Gradient Pruning","created":"2022-02-01","cited_by_count":8},{"id":"2305.07491","title":"A Comprehensive Analysis of Adapter Efficiency","created":"2023-05-12","cited_by_count":8},{"id":"2309.12078","title":"Clustering-based Domain-Incremental Learning","created":"2023-09-21","cited_by_count":8},{"id":"2501.01849","title":"A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses","created":"2025-01-03","cited_by_count":8},{"id":"1805.09092","title":"Excitation Dropout: Encouraging Plasticity in Deep Neural Networks","created":"2018-05-23","cited_by_count":7},{"id":"1911.12675","title":"Continuous Dropout","created":"2019-11-28","cited_by_count":7},{"id":"2211.01484","title":"Data Level Lottery Ticket Hypothesis for Vision Transformers","created":"2022-11-02","cited_by_count":7},{"id":"2405.17081","title":"Effective Layer Pruning Through Similarity Metric Perspective","created":"2024-05-27","cited_by_count":6},{"id":"2502.01342","title":"Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss","created":"2025-02-03","cited_by_count":6},{"id":"2406.04549","title":"Concurrent Training and Layer Pruning of Deep Neural Networks","created":"2024-06-06","cited_by_count":5},{"id":"2405.14147","title":"Minimum number of neurons in fully connected layers of a given neural network (the first approximation)","created":"2024-05-23","cited_by_count":3},{"id":"2405.20620","title":"\"Forgetting\" in Machine Learning and Beyond: A Survey","created":"2024-05-31","cited_by_count":2},{"id":"1911.07012","title":"Three cooperative mechanisms required for recovery after brain damage","created":"2019-11-16","cited_by_count":1},{"id":"2311.17943","title":"LayerCollapse: Adaptive compression of neural networks","created":"2023-11-29","cited_by_count":1},{"id":"2405.07295","title":"Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer","created":"2024-05-12","cited_by_count":1},{"id":"2409.17640","title":"T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task","created":"2024-09-26","cited_by_count":1}],"next_cursor":null}