{"list":"other","total":101,"rows":[{"id":"1806.07572","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","created":"2018-06-20","cited_by_count":2621},{"id":"2010.01412","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","created":"2020-10-03","cited_by_count":1417},{"id":"1609.04836","title":"On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima","created":"2016-09-15","cited_by_count":1352},{"id":"1803.05407","title":"Averaging Weights Leads to Wider Optima and Better Generalization","created":"2018-03-14","cited_by_count":1209},{"id":"1712.09913","title":"Visualizing the Loss Landscape of Neural Nets","created":"2017-12-28","cited_by_count":1130},{"id":"1901.09948","title":"Surrogate Gradient Learning in Spiking Neural Networks","created":"2019-01-28","cited_by_count":626},{"id":"1706.08947","title":"Exploring Generalization in Deep Learning","created":"2017-06-27","cited_by_count":600},{"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":"1912.02178","title":"Fantastic Generalization Measures and Where to Find Them","created":"2019-12-04","cited_by_count":513},{"id":"1703.04933","title":"Sharp Minima Can Generalize For Deep Nets","created":"2017-03-15","cited_by_count":466},{"id":"2103.00065","title":"Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability","created":"2021-02-26","cited_by_count":427},{"id":"1611.01838","title":"Entropy-SGD: Biasing Gradient Descent Into Wide Valleys","created":"2016-11-06","cited_by_count":359},{"id":"1901.10159","title":"An Investigation into Neural Net Optimization via Hessian Eigenvalue Density","created":"2019-01-29","cited_by_count":292},{"id":"1912.07145","title":"PyHessian: Neural Networks Through the Lens of the Hessian","created":"2019-12-16","cited_by_count":292},{"id":"1706.04454","title":"Empirical Analysis of the Hessian of Over-Parametrized Neural Networks","created":"2017-06-14","cited_by_count":290},{"id":"1705.08741","title":"Train longer, generalize better: closing the generalization gap in large batch training of neural networks","created":"2017-05-24","cited_by_count":279},{"id":"2011.09468","title":"Gradient Starvation: A Learning Proclivity in Neural Networks","created":"2020-11-18","cited_by_count":267},{"id":"1710.03667","title":"High-dimensional dynamics of generalization error in neural networks","created":"2017-10-10","cited_by_count":241},{"id":"2003.02218","title":"The large learning rate phase of deep learning: the catapult mechanism","created":"2020-03-04","cited_by_count":216},{"id":"1802.08760","title":"Sensitivity and Generalization in Neural Networks: an Empirical Study","created":"2018-02-23","cited_by_count":189},{"id":"1810.10531","title":"A mathematical theory of semantic development in deep neural networks","created":"2018-10-23","cited_by_count":186},{"id":"1705.11146","title":"SuperSpike: Supervised learning in multi-layer spiking neural networks","created":"2017-05-31","cited_by_count":182},{"id":"1803.09574","title":"Long short-term memory and learning-to-learn in networks of spiking neurons","created":"2018-03-26","cited_by_count":181},{"id":"1907.04595","title":"Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks","created":"2019-07-10","cited_by_count":162},{"id":"1611.07476","title":"Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond","created":"2016-11-22","cited_by_count":159},{"id":"2002.09572","title":"The Break-Even Point on Optimization Trajectories of Deep Neural Networks","created":"2020-02-21","cited_by_count":150},{"id":"1710.06451","title":"A Bayesian Perspective on Generalization and Stochastic Gradient Descent","created":"2017-10-17","cited_by_count":135},{"id":"2205.09745","title":"Understanding Gradient Descent on Edge of Stability in Deep Learning","created":"2022-05-19","cited_by_count":128},{"id":"1904.13262","title":"Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks","created":"2019-04-30","cited_by_count":124},{"id":"1711.01530","title":"Fisher-Rao Metric, Geometry, and Complexity of Neural Networks","created":"2017-11-05","cited_by_count":100},{"id":"2002.10085","title":"Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks","created":"2020-02-24","cited_by_count":100},{"id":"1810.11393","title":"Dendritic cortical microcircuits approximate the backpropagation algorithm","created":"2018-10-26","cited_by_count":97},{"id":"1807.05031","title":"On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length","created":"2018-07-13","cited_by_count":88},{"id":"2001.00939","title":"Relative Flatness and Generalization","created":"2020-01-03","cited_by_count":88},{"id":"1802.08241","title":"Hessian-based Analysis of Large Batch Training and Robustness to Adversaries","created":"2018-02-22","cited_by_count":87},{"id":"2010.04925","title":"Regularizing Neural Networks via Adversarial Model Perturbation","created":"2020-10-10","cited_by_count":78},{"id":"1801.05219","title":"Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules","created":"2018-01-16","cited_by_count":67},{"id":"2006.03824","title":"Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias","created":"2020-06-06","cited_by_count":64},{"id":"1901.04653","title":"Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis","created":"2019-01-15","cited_by_count":59},{"id":"1706.04698","title":"Gradient Descent for Spiking Neural Networks","created":"2017-06-14","cited_by_count":48},{"id":"1702.05043","title":"Unbiased Online Recurrent Optimization","created":"2017-02-16","cited_by_count":47},{"id":"1905.07833","title":"Shaping the learning landscape in neural networks around wide flat minima","created":"2019-05-20","cited_by_count":47},{"id":"2007.02686","title":"Meta-Learning through Hebbian Plasticity in Random Networks","created":"2020-07-06","cited_by_count":47},{"id":"2010.08262","title":"Local plasticity rules can learn deep representations using self-supervised contrastive predictions","created":"2020-10-16","cited_by_count":47},{"id":"1906.07774","title":"On the interplay between noise and curvature and its effect on optimization and generalization","created":"2019-06-18","cited_by_count":44},{"id":"2012.03801","title":"A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization","created":"2020-12-07","cited_by_count":44},{"id":"2202.02831","title":"Anticorrelated Noise Injection for Improved Generalization","created":"2022-02-06","cited_by_count":42},{"id":"2012.07976","title":"NeurIPS 2020 Competition: Predicting Generalization in Deep Learning","created":"2020-12-14","cited_by_count":41},{"id":"2105.08810","title":"Sparse Spiking Gradient Descent","created":"2021-05-18","cited_by_count":41},{"id":"2109.14247","title":"Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State","created":"2021-09-29","cited_by_count":40},{"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":"2006.07239","title":"Surrogate gradients for analog neuromorphic computing","created":"2020-06-12","cited_by_count":38},{"id":"2010.11931","title":"Brain-Inspired Learning on Neuromorphic Substrates","created":"2020-10-22","cited_by_count":37},{"id":"2201.13415","title":"Towards Scaling Difference Target Propagation by Learning Backprop Targets","created":"2022-01-31","cited_by_count":34},{"id":"2103.01519","title":"Hessian Eigenspectra of More Realistic Nonlinear Models","created":"2021-03-02","cited_by_count":33},{"id":"1805.10842","title":"Approximating Real-Time Recurrent Learning with Random Kronecker Factors","created":"2018-05-28","cited_by_count":29},{"id":"2106.07887","title":"Credit Assignment in Neural Networks through Deep Feedback Control","created":"2021-06-15","cited_by_count":29},{"id":"2006.05620","title":"Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption","created":"2020-06-10","cited_by_count":26},{"id":"1906.02926","title":"The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks","created":"2019-06-07","cited_by_count":20},{"id":"2005.11362","title":"Stable and expressive recurrent vision models","created":"2020-05-22","cited_by_count":20},{"id":"2006.11036","title":"The interplay between randomness and structure during learning in RNNs","created":"2020-06-19","cited_by_count":20},{"id":"2011.02159","title":"Reverse engineering learned optimizers reveals known and novel mechanisms","created":"2020-11-04","cited_by_count":19},{"id":"2106.04089","title":"Credit Assignment Through Broadcasting a Global Error Vector","created":"2021-06-08","cited_by_count":19},{"id":"2006.07232","title":"A Practical Sparse Approximation for Real Time Recurrent Learning","created":"2020-06-12","cited_by_count":18},{"id":"2111.01256","title":"Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems","created":"2021-11-01","cited_by_count":18},{"id":"1902.01028","title":"Can SGD Learn Recurrent Neural Networks with Provable Generalization?","created":"2019-02-04","cited_by_count":17},{"id":"1910.00195","title":"How noise affects the Hessian spectrum in overparameterized neural networks","created":"2019-10-01","cited_by_count":17},{"id":"2202.05808","title":"Investigating Power laws in Deep Representation Learning","created":"2022-02-11","cited_by_count":17},{"id":"1710.03070","title":"full-FORCE: A Target-Based Method for Training Recurrent Networks","created":"2017-10-09","cited_by_count":16},{"id":"1812.07965","title":"Deep learning with asymmetric connections and Hebbian updates","created":"2018-11-19","cited_by_count":16},{"id":"2006.07310","title":"Reservoir Computing meets Recurrent Kernels and Structured Transforms","created":"2020-06-12","cited_by_count":16},{"id":"1809.07402","title":"Identifying Generalization Properties in Neural Networks","created":"2018-09-19","cited_by_count":15},{"id":"2106.01177","title":"Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck","created":"2021-06-02","cited_by_count":15},{"id":"2110.14549","title":"Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons","created":"2021-10-27","cited_by_count":15},{"id":"2106.06044","title":"Convergence and Alignment of Gradient Descent with Random Backpropagation Weights","created":"2021-06-10","cited_by_count":14},{"id":"1609.02545","title":"Supervised Learning in Spiking Neural Networks with FORCE Training","created":"2016-09-08","cited_by_count":13},{"id":"2106.13031","title":"Towards Biologically Plausible Convolutional Networks","created":"2021-06-22","cited_by_count":13},{"id":"1801.02254","title":"Theory of Deep Learning IIb: Optimization Properties of SGD","created":"2018-01-07","cited_by_count":12},{"id":"2111.01760","title":"Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity","created":"2021-10-26","cited_by_count":12},{"id":"2111.06979","title":"Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception","created":"2021-11-12","cited_by_count":12},{"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":"1811.01768","title":"A Biologically Plausible Learning Rule for Deep Learning in the Brain","created":"2018-11-05","cited_by_count":9},{"id":"2010.11765","title":"Identifying Learning Rules From Neural Network Observables","created":"2020-10-22","cited_by_count":9},{"id":"1902.02434","title":"A Scale Invariant Flatness Measure for Deep Network Minima","created":"2019-02-06","cited_by_count":8},{"id":"1911.00307","title":"Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity","created":"2019-11-01","cited_by_count":8},{"id":"2111.08858","title":"A Normative and Biologically Plausible Algorithm for Independent Component Analysis","created":"2021-11-17","cited_by_count":8},{"id":"2111.09356","title":"Charting and navigating the space of solutions for recurrent neural networks","created":"2021-11-17","cited_by_count":8},{"id":"1902.02405","title":"On the Variance of Unbiased Online Recurrent Optimization","created":"2019-02-06","cited_by_count":7},{"id":"2010.12660","title":"A simple normative network approximates local non-Hebbian learning in the cortex","created":"2020-10-23","cited_by_count":6},{"id":"2009.01269","title":"Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees","created":"2020-09-02","cited_by_count":5},{"id":"2011.07334","title":"Using noise to probe recurrent neural network structure and prune synapses","created":"2020-11-14","cited_by_count":5},{"id":"2111.06920","title":"Neural optimal feedback control with local learning rules","created":"2021-11-12","cited_by_count":5},{"id":"2010.06247","title":"Unfolding recurrence by Green's functions for optimized reservoir computing","created":"2020-10-13","cited_by_count":4},{"id":"2106.15685","title":"Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity","created":"2021-06-29","cited_by_count":3},{"id":"2004.07975","title":"New Light on Cortical Neuropeptides and Synaptic Network Plasticity","created":"2020-04-16","cited_by_count":2},{"id":"2006.14178","title":"Predictive coding in balanced neural networks with noise, chaos and delays","created":"2020-06-25","cited_by_count":2},{"id":"2110.09804","title":"Probing the relationship between linear dynamical systems and low-rank recurrent neural network models","created":"2021-10-19","cited_by_count":2},{"id":"1505.00655","title":"Astrocytes: orchestrating synaptic plasticity?","created":"2015-05-04","cited_by_count":1},{"id":"1905.12100","title":"Using local plasticity rules to train recurrent neural networks","created":"2019-05-28","cited_by_count":1}],"next_cursor":"100"}