{"list":"other","total":63,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1702.05747","title":"A Survey on Deep Learning in Medical Image Analysis","created":"2017-02-19","cited_by_count":1186},{"id":"1511.07289","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","created":"2015-11-23","cited_by_count":1070},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1404.7828","title":"Deep Learning in Neural Networks: An Overview","created":"2014-04-30","cited_by_count":770},{"id":"1902.06162","title":"Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey","created":"2019-02-16","cited_by_count":657},{"id":"1706.02515","title":"Self-Normalizing Neural Networks","created":"2017-06-08","cited_by_count":598},{"id":"1912.12033","title":"Deep Learning for 3D Point Clouds: A Survey","created":"2019-12-27","cited_by_count":558},{"id":"1807.05511","title":"Object Detection with Deep Learning: A Review","created":"2018-07-15","cited_by_count":528},{"id":"1505.00853","title":"Empirical Evaluation of Rectified Activations in Convolutional Network","created":"2015-05-05","cited_by_count":519},{"id":"2003.08271","title":"Pre-trained Models for Natural Language Processing: A Survey","created":"2020-03-18","cited_by_count":505},{"id":"1708.05866","title":"A Brief Survey of Deep Reinforcement Learning","created":"2017-08-19","cited_by_count":483},{"id":"1703.09039","title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","created":"2017-03-27","cited_by_count":434},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"1804.08150","title":"Deep Learning in Spiking Neural Networks","created":"2018-04-22","cited_by_count":263},{"id":"1901.06032","title":"A Survey of the Recent Architectures of Deep Convolutional Neural Networks","created":"2019-01-17","cited_by_count":213},{"id":"1302.4389","title":"Maxout Networks","created":"2013-02-18","cited_by_count":187},{"id":"2003.03488","title":"ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions","created":"2020-03-07","cited_by_count":184},{"id":"1807.11809","title":"Deep learning in agriculture: A survey","created":"2018-07-31","cited_by_count":160},{"id":"1808.03344","title":"Deep Learning for Single Image Super-Resolution: A Brief Review","created":"2018-08-09","cited_by_count":158},{"id":"2109.14545","title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","created":"2021-09-29","cited_by_count":155},{"id":"1811.10052","title":"An overview of deep learning in medical imaging focusing on MRI","created":"2018-11-25","cited_by_count":140},{"id":"1912.13171","title":"Deep Learning on Image Denoising: An overview","created":"2019-12-31","cited_by_count":128},{"id":"1912.12693","title":"A Gentle Introduction to Deep Learning for Graphs","created":"2019-12-29","cited_by_count":108},{"id":"1704.06825","title":"Deep Learning for Medical Image Processing: Overview, Challenges and Future","created":"2017-04-22","cited_by_count":104},{"id":"1709.02250","title":"Medical Image Analysis using Convolutional Neural Networks: A Review","created":"2017-09-04","cited_by_count":99},{"id":"2005.00817","title":"A survey on modern trainable activation functions","created":"2020-05-02","cited_by_count":89},{"id":"1704.07483","title":"Continuously Differentiable Exponential Linear Units","created":"2017-04-24","cited_by_count":73},{"id":"1603.06430","title":"Deep Learning in Bioinformatics","created":"2016-03-21","cited_by_count":48},{"id":"1701.04503","title":"Deep Learning for Computational Chemistry","created":"2017-01-17","cited_by_count":38},{"id":"1906.09529","title":"Learning Activation Functions: A new paradigm for understanding Neural Networks","created":"2019-06-23","cited_by_count":34},{"id":"1409.6070","title":"Spatially-sparse convolutional neural networks","created":"2014-09-22","cited_by_count":33},{"id":"1605.09332","title":"Parametric Exponential Linear Unit for Deep Convolutional Neural Networks","created":"2016-05-30","cited_by_count":32},{"id":"2007.09479","title":"Deep Learning Based Brain Tumor Segmentation: A Survey","created":"2020-07-18","cited_by_count":30},{"id":"1512.07030","title":"Deep Learning with S-shaped Rectified Linear Activation Units","created":"2015-12-22","cited_by_count":26},{"id":"2110.05626","title":"Parameterizing Activation Functions for Adversarial Robustness","created":"2021-10-11","cited_by_count":22},{"id":"1801.09403","title":"Learning Combinations of Activation Functions","created":"2018-01-29","cited_by_count":20},{"id":"2102.01732","title":"Truly Sparse Neural Networks at Scale","created":"2021-02-02","cited_by_count":16},{"id":"2012.00938","title":"Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution","created":"2020-12-02","cited_by_count":15},{"id":"1806.10064","title":"Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks","created":"2018-06-26","cited_by_count":13},{"id":"1602.05980","title":"Revise Saturated Activation Functions","created":"2016-02-18","cited_by_count":11},{"id":"1809.09534","title":"PLU: The Piecewise Linear Unit Activation Function","created":"2018-09-03","cited_by_count":11},{"id":"1902.03306","title":"A simple and efficient architecture for trainable activation functions","created":"2019-02-08","cited_by_count":10},{"id":"2006.13858","title":"AReLU: Attention-based Rectified Linear Unit","created":"2020-06-24","cited_by_count":9},{"id":"1907.05794","title":"ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval","created":"2019-07-12","cited_by_count":7},{"id":"1606.00305","title":"Improving Deep Neural Network with Multiple Parametric Exponential Linear Units","created":"2016-06-01","cited_by_count":6},{"id":"1801.07145","title":"E-swish: Adjusting Activations to Different Network Depths","created":"2018-01-22","cited_by_count":6},{"id":"1804.02051","title":"Average Biased ReLU Based CNN Descriptor for Improved Face Retrieval","created":"2018-04-02","cited_by_count":6},{"id":"1702.07790","title":"Activation Ensembles for Deep Neural Networks","created":"2017-02-24","cited_by_count":5},{"id":"1911.03925","title":"Symmetrical Gaussian Error Linear Units (SGELUs)","created":"2019-11-10","cited_by_count":5},{"id":"2302.10913","title":"From paintbrush to pixel: A review of deep neural networks in AI-generated art","created":"2023-02-14","cited_by_count":5},{"id":"1604.00676","title":"Multi-Bias Non-linear Activation in Deep Neural Networks","created":"2016-04-03","cited_by_count":4},{"id":"1812.06247","title":"Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning","created":"2018-12-15","cited_by_count":3},{"id":"1912.10752","title":"Learn-able parameter guided Activation Functions","created":"2019-12-23","cited_by_count":2},{"id":"2204.12920","title":"Trainable Compound Activation Functions for Machine Learning","created":"2022-04-25","cited_by_count":2},{"id":"2301.05993","title":"Empirical study of the modulus as activation function in computer vision applications","created":"2023-01-15","cited_by_count":2},{"id":"2309.07481","title":"Improved Auto-Encoding using Deterministic Projected Belief Networks","created":"2023-09-14","cited_by_count":2},{"id":"2011.03155","title":"Parametric Flatten-T Swish: An Adaptive Non-linear Activation Function For Deep Learning","created":"2020-11-06","cited_by_count":1},{"id":"2108.00700","title":"Piecewise Linear Units Improve Deep Neural Networks","created":"2021-08-02","cited_by_count":1}],"next_cursor":null}