{"list":"other","total":57,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1611.04076","title":"Least Squares Generative Adversarial Networks","created":"2016-11-13","cited_by_count":1001},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"1604.06174","title":"Training Deep Nets with Sublinear Memory Cost","created":"2016-04-21","cited_by_count":848},{"id":"1312.6120","title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","created":"2013-12-20","cited_by_count":763},{"id":"1907.10786","title":"Interpreting the Latent Space of GANs for Semantic Face Editing","created":"2019-07-25","cited_by_count":695},{"id":"1701.07875","title":"Wasserstein GAN","created":"2017-01-26","cited_by_count":639},{"id":"1810.01367","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","created":"2018-10-02","cited_by_count":594},{"id":"1708.03888","title":"Large Batch Training of Convolutional Networks","created":"2017-08-13","cited_by_count":535},{"id":"1806.00069","title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","created":"2018-05-31","cited_by_count":508},{"id":"1606.05908","title":"Tutorial on Variational Autoencoders","created":"2016-06-19","cited_by_count":409},{"id":"1512.09300","title":"Autoencoding beyond pixels using a learned similarity metric","created":"2015-12-31","cited_by_count":407},{"id":"1811.00995","title":"Invertible Residual Networks","created":"2018-11-02","cited_by_count":365},{"id":"1605.09782","title":"Adversarial Feature Learning","created":"2016-05-31","cited_by_count":350},{"id":"1804.03599","title":"Understanding disentangling in $\\beta$-VAE","created":"2018-04-10","cited_by_count":335},{"id":"1808.04730","title":"Analyzing Inverse Problems with Invertible Neural Networks","created":"2018-08-14","cited_by_count":242},{"id":"1602.02644","title":"Generating Images with Perceptual Similarity Metrics based on Deep Networks","created":"2016-02-08","cited_by_count":222},{"id":"1707.04585","title":"The Reversible Residual Network: Backpropagation Without Storing Activations","created":"2017-07-14","cited_by_count":217},{"id":"2001.06937","title":"A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications","created":"2020-01-20","cited_by_count":209},{"id":"1605.09304","title":"Synthesizing the preferred inputs for neurons in neural networks via deep generator networks","created":"2016-05-30","cited_by_count":181},{"id":"1911.02469","title":"Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse","created":"2019-11-06","cited_by_count":121},{"id":"1602.03616","title":"Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks","created":"2016-02-11","cited_by_count":106},{"id":"1512.02017","title":"Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images","created":"2015-12-07","cited_by_count":93},{"id":"2001.02522","title":"On Interpretability of Artificial Neural Networks: A Survey","created":"2020-01-08","cited_by_count":88},{"id":"1810.00597","title":"Taming VAEs","created":"2018-10-01","cited_by_count":85},{"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":"2001.05992","title":"Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks","created":"2020-01-16","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":"1904.10631","title":"Low-Memory Neural Network Training: A Technical Report","created":"2019-04-24","cited_by_count":57},{"id":"1806.10181","title":"Unsupervised Learning by Competing Hidden Units","created":"2018-06-26","cited_by_count":44},{"id":"1910.13233","title":"Neural Density Estimation and Likelihood-free Inference","created":"2019-10-29","cited_by_count":23},{"id":"2012.03837","title":"Parallel Training of Deep Networks with Local Updates","created":"2020-12-07","cited_by_count":19},{"id":"2102.01792","title":"A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks","created":"2021-02-02","cited_by_count":14},{"id":"2103.03725","title":"Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks","created":"2021-03-05","cited_by_count":14},{"id":"1803.01834","title":"Conducting Credit Assignment by Aligning Local Representations","created":"2018-03-05","cited_by_count":12},{"id":"1906.03591","title":"A Survey on Neural Network Language Models","created":"2019-06-09","cited_by_count":12},{"id":"2004.14758","title":"Preventing Posterior Collapse with Levenshtein Variational Autoencoder","created":"2020-04-30","cited_by_count":12},{"id":"1506.06472","title":"A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation","created":"2015-06-22","cited_by_count":11},{"id":"2005.13178","title":"Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments","created":"2020-05-27","cited_by_count":11},{"id":"2006.02824","title":"Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map","created":"2020-06-04","cited_by_count":11},{"id":"2006.09549","title":"Learning to Learn with Feedback and Local Plasticity","created":"2020-06-16","cited_by_count":11},{"id":"2009.05359","title":"Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain","created":"2020-09-11","cited_by_count":11},{"id":"1912.05127","title":"A Closer Look at Disentangling in $\\beta$-VAE","created":"2019-12-11","cited_by_count":8},{"id":"2012.08795","title":"Study on the Large Batch Size Training of Neural Networks Based on the Second Order Gradient","created":"2020-12-16","cited_by_count":3},{"id":"1603.02518","title":"A New Method to Visualize Deep Neural Networks","created":"2016-03-08","cited_by_count":2},{"id":"1806.01610","title":"Training Generative Reversible Networks","created":"2018-06-05","cited_by_count":2},{"id":"1810.09284","title":"Gradient target propagation","created":"2018-10-19","cited_by_count":1}],"next_cursor":null}