{"list":"other","total":71,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"2102.05918","title":"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision","created":"2021-02-11","cited_by_count":3864},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"1608.08710","title":"Pruning Filters for Efficient ConvNets","created":"2016-08-31","cited_by_count":1143},{"id":"1807.05520","title":"Deep Clustering for Unsupervised Learning of Visual Features","created":"2018-07-15","cited_by_count":1028},{"id":"1703.07015","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","created":"2017-03-21","cited_by_count":879},{"id":"2002.08546","title":"Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation","created":"2020-02-20","cited_by_count":848},{"id":"1706.08498","title":"Spectrally-normalized margin bounds for neural networks","created":"2017-06-26","cited_by_count":686},{"id":"2403.14608","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","created":"2024-03-21","cited_by_count":656},{"id":"1703.11008","title":"Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data","created":"2017-03-31","cited_by_count":533},{"id":"2102.00554","title":"Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks","created":"2021-01-31","cited_by_count":527},{"id":"1912.02178","title":"Fantastic Generalization Measures and Where to Find Them","created":"2019-12-04","cited_by_count":513},{"id":"1707.09564","title":"A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks","created":"2017-07-29","cited_by_count":314},{"id":"1703.02930","title":"Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks","created":"2017-03-08","cited_by_count":311},{"id":"1404.0736","title":"Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation","created":"2014-04-02","cited_by_count":306},{"id":"1405.3866","title":"Speeding up Convolutional Neural Networks with Low Rank Expansions","created":"2014-05-15","cited_by_count":284},{"id":"1509.06569","title":"Tensorizing Neural Networks","created":"2015-09-22","cited_by_count":250},{"id":"1306.0543","title":"Predicting Parameters in Deep Learning","created":"2013-06-03","cited_by_count":224},{"id":"2004.04393","title":"Universal Source-Free Domain Adaptation","created":"2020-04-09","cited_by_count":220},{"id":"1412.6553","title":"Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition","created":"2014-12-19","cited_by_count":203},{"id":"2110.04202","title":"Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation","created":"2021-10-08","cited_by_count":177},{"id":"2108.01614","title":"Generalized Source-free Domain Adaptation","created":"2021-08-03","cited_by_count":175},{"id":"2012.07297","title":"Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer","created":"2020-12-14","cited_by_count":173},{"id":"2103.16372","title":"Source-Free Domain Adaptation for Semantic Segmentation","created":"2021-03-30","cited_by_count":168},{"id":"1511.06530","title":"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications","created":"2015-11-20","cited_by_count":162},{"id":"2212.10650","title":"KronA: Parameter Efficient Tuning with Kronecker Adapter","created":"2022-12-20","cited_by_count":133},{"id":"1804.05862","title":"Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach","created":"2018-04-16","cited_by_count":126},{"id":"2312.13558","title":"The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction","created":"2023-12-21","cited_by_count":123},{"id":"2309.14859","title":"Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation","created":"2023-09-26","cited_by_count":109},{"id":"2204.00595","title":"Monarch: Expressive Structured Matrices for Efficient and Accurate Training","created":"2022-04-01","cited_by_count":106},{"id":"2302.11803","title":"A Comprehensive Survey on Source-free Domain Adaptation","created":"2023-02-23","cited_by_count":104},{"id":"1511.06067","title":"Convolutional neural networks with low-rank regularization","created":"2015-11-19","cited_by_count":103},{"id":"2204.11257","title":"Source-Free Domain Adaptation via Distribution Estimation","created":"2022-04-24","cited_by_count":97},{"id":"2205.04183","title":"Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation","created":"2022-05-09","cited_by_count":94},{"id":"2403.00131","title":"UniTS: A Unified Multi-Task Time Series Model","created":"2024-02-29","cited_by_count":94},{"id":"2302.14829","title":"Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting","created":"2023-02-22","cited_by_count":93},{"id":"2007.01524","title":"Domain Adaptation without Source Data","created":"2020-07-03","cited_by_count":88},{"id":"1906.09777","title":"A Tensorized Transformer for Language Modeling","created":"2019-06-24","cited_by_count":86},{"id":"2005.10996","title":"Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data","created":"2020-05-22","cited_by_count":82},{"id":"2108.11249","title":"Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation","created":"2021-08-25","cited_by_count":81},{"id":"2102.06828","title":"Domain Adaptation for Time Series Forecasting via Attention Sharing","created":"2021-02-13","cited_by_count":68},{"id":"2303.03770","title":"Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation","created":"2023-03-07","cited_by_count":65},{"id":"2302.03133","title":"Domain Adaptation for Time Series Under Feature and Label Shifts","created":"2023-02-06","cited_by_count":60},{"id":"2206.08009","title":"Balancing Discriminability and Transferability for Source-Free Domain Adaptation","created":"2022-06-16","cited_by_count":58},{"id":"2311.16510","title":"Source-Free Domain Adaptation with Frozen Multimodal Foundation Model","created":"2023-11-27","cited_by_count":55},{"id":"2012.11797","title":"Time Series Domain Adaptation via Sparse Associative Structure Alignment","created":"2020-12-22","cited_by_count":51},{"id":"2303.17132","title":"C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation","created":"2023-03-30","cited_by_count":50},{"id":"2002.08253","title":"Distance-Based Regularisation of Deep Networks for Fine-Tuning","created":"2020-02-19","cited_by_count":48},{"id":"2108.03152","title":"Source-Free Domain Adaptation for Image Segmentation","created":"2021-08-06","cited_by_count":45},{"id":"2111.04578","title":"Improved Regularization and Robustness for Fine-tuning in Neural Networks","created":"2021-11-08","cited_by_count":45},{"id":"2203.08321","title":"ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data","created":"2022-03-15","cited_by_count":45},{"id":"2206.06243","title":"Contrastive Learning for Unsupervised Domain Adaptation of Time Series","created":"2022-06-13","cited_by_count":36},{"id":"1712.05134","title":"Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition","created":"2017-12-14","cited_by_count":33},{"id":"2111.14834","title":"Self-supervised Autoregressive Domain Adaptation for Time Series Data","created":"2021-11-29","cited_by_count":29},{"id":"2406.06248","title":"Compute Better Spent: Replacing Dense Layers with Structured Matrices","created":"2024-06-10","cited_by_count":28},{"id":"2403.01493","title":"ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis","created":"2024-03-03","cited_by_count":27},{"id":"2011.14439","title":"Scaling Down Deep Learning with MNIST-1D","created":"2020-11-29","cited_by_count":25},{"id":"2109.14778","title":"CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning","created":"2021-09-30","cited_by_count":23},{"id":"2405.02358","title":"Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey","created":"2024-05-03","cited_by_count":23},{"id":"2207.13247","title":"Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation","created":"2022-07-27","cited_by_count":21},{"id":"2307.07542","title":"Source-Free Domain Adaptation with Temporal Imputation for Time Series Data","created":"2023-07-14","cited_by_count":15},{"id":"2312.15230","title":"PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs","created":"2023-12-23","cited_by_count":14},{"id":"2302.03284","title":"Unsupervised Deep Learning for IoT Time Series","created":"2023-02-07","cited_by_count":11},{"id":"1903.06133","title":"Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation","created":"2019-03-14","cited_by_count":9},{"id":"2309.00528","title":"Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering","created":"2023-09-01","cited_by_count":9},{"id":"2410.02081","title":"MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameters","created":"2024-10-02","cited_by_count":9},{"id":"2211.12624","title":"Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization","created":"2022-11-22","cited_by_count":6},{"id":"2304.00792","title":"Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation","created":"2023-04-03","cited_by_count":6},{"id":"2406.02635","title":"Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation","created":"2024-06-04","cited_by_count":6}],"next_cursor":null}