{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"2005.10242","title":"Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere","created":"2020-05-20","cited_by_count":1405},{"id":"1610.01644","title":"Understanding intermediate layers using linear classifier probes","created":"2016-10-05","cited_by_count":1353},{"id":"1906.05849","title":"Contrastive Multiview Coding","created":"2019-06-13","cited_by_count":1301},{"id":"1808.06670","title":"Learning deep representations by mutual information estimation and maximization","created":"2018-08-20","cited_by_count":1273},{"id":"1604.06174","title":"Training Deep Nets with Sublinear Memory Cost","created":"2016-04-21","cited_by_count":848},{"id":"1503.02406","title":"Deep Learning and the Information Bottleneck Principle","created":"2015-03-09","cited_by_count":802},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1806.08342","title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","created":"2018-06-21","cited_by_count":521},{"id":"1907.13625","title":"On Mutual Information Maximization for Representation Learning","created":"2019-07-31","cited_by_count":316},{"id":"2212.13345","title":"The Forward-Forward Algorithm: Some Preliminary Investigations","created":"2022-12-27","cited_by_count":266},{"id":"1404.5997","title":"One weird trick for parallelizing convolutional neural networks","created":"2014-04-23","cited_by_count":264},{"id":"2101.09671","title":"Pruning and Quantization for Deep Neural Network Acceleration: A Survey","created":"2021-01-24","cited_by_count":263},{"id":"1609.01596","title":"Direct Feedback Alignment Provides Learning in Deep Neural Networks","created":"2016-09-06","cited_by_count":240},{"id":"1706.01350","title":"Emergence of Invariance and Disentanglement in Deep Representations","created":"2017-06-05","cited_by_count":215},{"id":"2002.07017","title":"Learning Robust Representations via Multi-View Information Bottleneck","created":"2020-02-17","cited_by_count":191},{"id":"1412.7525","title":"Difference Target Propagation","created":"2014-12-23","cited_by_count":148},{"id":"1901.06656","title":"Training Neural Networks with Local Error Signals","created":"2019-01-20","cited_by_count":136},{"id":"1812.11446","title":"Greedy Layerwise Learning Can Scale to ImageNet","created":"2018-12-29","cited_by_count":115},{"id":"1807.04587","title":"Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures","created":"2018-07-12","cited_by_count":101},{"id":"1902.01889","title":"Analyzing and Improving Representations with the Soft Nearest Neighbor Loss","created":"2019-02-05","cited_by_count":95},{"id":"1902.01996","title":"Are All Layers Created Equal?","created":"2019-02-06","cited_by_count":94},{"id":"2305.18887","title":"How Does Information Bottleneck Help Deep Learning?","created":"2023-05-30","cited_by_count":93},{"id":"1905.11786","title":"Putting An End to End-to-End: Gradient-Isolated Learning of Representations","created":"2019-05-28","cited_by_count":86},{"id":"1407.7906","title":"How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation","created":"2014-07-29","cited_by_count":79},{"id":"1901.08164","title":"Decoupled Greedy Learning of CNNs","created":"2019-01-23","cited_by_count":78},{"id":"1908.01580","title":"The HSIC Bottleneck: Deep Learning without Back-Propagation","created":"2019-08-05","cited_by_count":77},{"id":"2106.08320","title":"Self-Supervised Learning with Kernel Dependence Maximization","created":"2021-06-15","cited_by_count":77},{"id":"2202.08587","title":"Gradients without Backpropagation","created":"2022-02-17","cited_by_count":73},{"id":"2101.10832","title":"Revisiting Locally Supervised Learning: an Alternative to End-to-end Training","created":"2021-01-26","cited_by_count":71},{"id":"1802.09766","title":"Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle","created":"2018-02-27","cited_by_count":70},{"id":"2209.11883","title":"Hebbian Deep Learning Without Feedback","created":"2022-09-23","cited_by_count":61},{"id":"2008.01342","title":"LoCo: Local Contrastive Representation Learning","created":"2020-08-04","cited_by_count":55},{"id":"1411.0247","title":"Random feedback weights support learning in deep neural networks","created":"2014-11-02","cited_by_count":47},{"id":"1711.06756","title":"Deep supervised learning using local errors","created":"2017-11-17","cited_by_count":41},{"id":"2003.09671","title":"On Information Plane Analyses of Neural Network Classifiers -- A Review","created":"2020-03-21","cited_by_count":37},{"id":"2305.19753","title":"The Tunnel Effect: Building Data Representations in Deep Neural Networks","created":"2023-05-31","cited_by_count":34},{"id":"1706.04964","title":"Learning Deep ResNet Blocks Sequentially using Boosting Theory","created":"2017-06-15","cited_by_count":32},{"id":"2311.02960","title":"Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination","created":"2023-11-06","cited_by_count":32},{"id":"2107.05747","title":"SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks","created":"2021-07-12","cited_by_count":30},{"id":"2006.07123","title":"Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks","created":"2020-06-12","cited_by_count":25},{"id":"2204.01723","title":"Signal Propagation: A Framework for Learning and Inference In a Forward Pass","created":"2022-04-04","cited_by_count":24},{"id":"2302.01647","title":"Blockwise Self-Supervised Learning at Scale","created":"2023-02-03","cited_by_count":22},{"id":"2106.02734","title":"Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness","created":"2021-06-04","cited_by_count":20},{"id":"1907.07384","title":"Feature Selection via Mutual Information: New Theoretical Insights","created":"2019-07-17","cited_by_count":17},{"id":"2010.04116","title":"Interlocking Backpropagation: Improving depthwise model-parallelism","created":"2020-10-08","cited_by_count":17},{"id":"1703.07115","title":"Layer-wise training of deep networks using kernel similarity","created":"2017-03-21","cited_by_count":15},{"id":"2106.12912","title":"Information Bottleneck: Exact Analysis of (Quantized) Neural Networks","created":"2021-06-24","cited_by_count":15},{"id":"2305.00380","title":"DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning","created":"2023-04-30","cited_by_count":15},{"id":"1902.09037","title":"Adaptive Estimators Show Information Compression in Deep Neural Networks","created":"2019-02-24","cited_by_count":14},{"id":"2210.04311","title":"Pruning Adversarially Robust Neural Networks without Adversarial Examples","created":"2022-10-09","cited_by_count":12},{"id":"1905.11722","title":"A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation","created":"2019-05-28","cited_by_count":11},{"id":"1712.08608","title":"Learning in the Machine: the Symmetries of the Deep Learning Channel","created":"2017-12-22","cited_by_count":10},{"id":"1708.03704","title":"Deep Incremental Boosting","created":"2017-08-11","cited_by_count":7},{"id":"2208.01134","title":"Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization","created":"2022-08-01","cited_by_count":7},{"id":"2003.06254","title":"What Information Does a ResNet Compress?","created":"2020-03-13","cited_by_count":5},{"id":"2101.03419","title":"Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods","created":"2021-01-09","cited_by_count":5},{"id":"2109.03554","title":"Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning","created":"2021-09-08","cited_by_count":5},{"id":"1906.05560","title":"Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation","created":"2019-06-13","cited_by_count":3}],"next_cursor":null}