Learning Curves in Large Neural Networks. - H. Sebastian Seung; Haim Sompolinsky; Naftali Tishby (COLT, 1991)

Query by Committee. - H. Sebastian Seung; Manfred Opper; Haim Sompolinsky (COLT, 1992)

Information, Prediction, and Query by Committee. - Yoav Freund; H. Sebastian Seung; Eli Shamir 001; Naftali Tishby (NIPS, 1992)

On-line Learning of Dichotomies. - N. Barkai; H. Sebastian Seung; Haim Sompolinsky (NIPS, 1994)

Learning from a Population of Hypotheses. - Michael J. Kearns; H. Sebastian Seung (Mach. Learn., 1995)

Unsupervised Learning by Convex and Conic Coding. - Daniel D. Lee; H. Sebastian Seung (NIPS, 1996)

Rigorous Learning Curve Bounds from Statistical Mechanics. - David Haussler; Michael J. Kearns; H. Sebastian Seung; Naftali Tishby (Mach. Learn., 1996)

A Neural Network Based Head Tracking System. - Daniel D. Lee; H. Sebastian Seung (NIPS, 1997)

Learning Generative Models with the Up-Propagation Algorithm. - Jong-Hoon Oh; H. Sebastian Seung (NIPS, 1997)

Learning Continuous Attractors in Recurrent Networks. - H. Sebastian Seung (NIPS, 1997)

Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks. - H. Sebastian Seung; Tom J. Richardson; J. C. Lagarias 001; John J. Hopfield (NIPS, 1997)

The Rectified Gaussian Distribution. - Nicholas D. Socci; Daniel D. Lee; H. Sebastian Seung (NIPS, 1997)

Selective Sampling Using the Query by Committee Algorithm. - Yoav Freund; H. Sebastian Seung; Eli Shamir 001; Naftali Tishby (Mach. Learn., 1997)

Continuous attractors and oculomotor control. - H. Sebastian Seung (Neural Networks, 1998)

Learning in Intelligent Embedded Systems. - Daniel D. Lee; H. Sebastian Seung (USENIX Workshop on Embedded Systems, 1999)

Spike-based Learning Rules and Stabilization of Persistent Neural Activity. - Xiaohui Xie; H. Sebastian Seung (NIPS, 1999)

Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. - Richard H. R. Hahnloser; H. Sebastian Seung (NIPS, 2000)

Algorithms for Non-negative Matrix Factorization. - Daniel D. Lee; H. Sebastian Seung (NIPS, 2000)

Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks. - Xiaohui Xie; Richard H. R. Hahnloser; H. Sebastian Seung (NIPS, 2000)

The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback. - H. Sebastian Seung; Daniel D. Lee; Ben Y. Reis; David W. Tank (J. Comput. Neurosci., 2000)

A theory of neural integration in the head-direction system. - Richard H. R. Hahnloser; Xiaohui Xie; H. Sebastian Seung (NIPS, 2001)

Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition. - Xiaohui Xie; Richard H. R. Hahnloser; H. Sebastian Seung (Neural Comput., 2002)

Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. - Richard H. R. Hahnloser; H. Sebastian Seung; Jean-Jacques E. Slotine (Neural Comput., 2003)

Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network. - Xiaohui Xie; H. Sebastian Seung (Neural Comput., 2003)

Actuating a Simple 3D Passive Dynamic Walker. - Russ Tedrake; Teresa Weirui Zhang; Ming-fai Fong; H. Sebastian Seung (ICRA, 2004)

Stochastic policy gradient reinforcement learning on a simple 3D biped. - Russ Tedrake; Teresa Weirui Zhang; H. Sebastian Seung (IROS, 2004)

BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. - Brett D. Mensh; Justin Werfel; H. Sebastian Seung (IEEE Trans. Biomed. Eng., 2004)

Representing Part-Whole Relationships in Recurrent Neural Networks. - Viren Jain; Valentin P. Zhigulin; H. Sebastian Seung (NIPS, 2005)

Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks. - Justin Werfel; Xiaohui Xie; H. Sebastian Seung (Neural Comput., 2005)

Neural voting machines. - Whitman Richards; H. Sebastian Seung; Galen Pickard (Neural Networks, 2006)

Supervised Learning of Image Restoration with Convolutional Networks. - Viren Jain; Joseph F. Murray; Fabian Roth; Srinivas C. Turaga; Valentin P. Zhigulin; Kevin L. Briggman; Moritz Helmstaedter; Winfried Denk; H. Sebastian Seung (ICCV, 2007)

Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC. - Dezhe Z. Jin; Fethi M. Ramazanoglu; H. Sebastian Seung (J. Comput. Neurosci., 2007)

Natural Image Denoising with Convolutional Networks. - Viren Jain; H. Sebastian Seung (NIPS, 2008)

Maximin affinity learning of image segmentation. - Srinivas C. Turaga; Kevin L. Briggman; Moritz Helmstaedter; Winfried Denk; H. Sebastian Seung (NIPS, 2009)

Operant Matching as a Nash Equilibrium of an Intertemporal Game. - Yonatan Loewenstein; Drazen Prelec; H. Sebastian Seung (Neural Comput., 2009)

Boundary Learning by Optimization with Topological Constraints. - Viren Jain; Benjamin Bollmann; Mark Richardson; Daniel R. Berger; Moritz Helmstaedter; Kevin L. Briggman; Winfried Denk; Jared B. Bowden; John M. Mendenhall; Wickliffe C. Abraham; Kristen M. Harris; Narayanan Kasthuri; et al. (CVPR, 2010)

Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation. - Srinivas C. Turaga; Joseph F. Murray; Viren Jain; Fabian Roth; Moritz Helmstaedter; Kevin L. Briggman; Winfried Denk; H. Sebastian Seung (Neural Comput., 2010)

Learning to Agglomerate Superpixel Hierarchies. - Viren Jain; Srinivas C. Turaga; Kevin L. Briggman; Moritz Helmstaedter; Winfried Denk; H. Sebastian Seung (NIPS, 2011)

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction. - Kisuk Lee; Aleksandar Zlateski; Ashwin Vishwanathan; H. Sebastian Seung (NIPS, 2015)

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection. - Kisuk Lee; Aleksandar Zlateski; Ashwin Vishwanathan; H. Sebastian Seung (CoRR, 2015)

Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph. - Aleksandar Zlateski; H. Sebastian Seung (CoRR, 2015)

ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-core and Many-Core Shared Memory Machines. - Aleksandar Zlateski; Kisuk Lee; H. Sebastian Seung (IPDPS, 2016)

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks. - Noah J. Apthorpe; Alexander J. Riordan; Rob E. Aguilar; Jan Homann; Yi Gu; David W. Tank; H. Sebastian Seung (NIPS, 2016)

ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs. - Aleksandar Zlateski; Kisuk Lee; H. Sebastian Seung (SC, 2016)

ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs. - Aleksandar Zlateski; Kisuk Lee; H. Sebastian Seung (CoRR, 2016)

Compile-time optimized and statically scheduled N-D convnet primitives for multi-core and many-core (Xeon Phi) CPUs. - Aleksandar Zlateski; H. Sebastian Seung (ICS, 2017)

An Error Detection and Correction Framework for Connectomics. - Jonathan Zung; Ignacio Tartavull; Kisuk Lee; H. Sebastian Seung (NIPS, 2017)

Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. - Ignacio Arganda-Carreras; Verena Kaynig; Curtis Rueden; Kevin W. Eliceiri; Johannes E. Schindelin; Albert Cardona; H. Sebastian Seung (Bioinform., 2017)

Deep Learning Improves Template Matching by Normalized Cross Correlation. - Davit Buniatyan; Thomas Macrina; Dodam Ih; Jonathan Zung; H. Sebastian Seung (CoRR, 2017)

Superhuman Accuracy on the SNEMI3D Connectomics Challenge. - Kisuk Lee; Jonathan Zung; Peter Li; Viren Jain; H. Sebastian Seung (CoRR, 2017)

A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination. - H. Sebastian Seung; Jonathan Zung (CoRR, 2017)

An Error Detection and Correction Framework for Connectomics. - Jonathan Zung; Ignacio Tartavull; H. Sebastian Seung (CoRR, 2017)

Scalable training of 3D convolutional networks on multi- and many-cores. - Aleksandar Zlateski; Kisuk Lee; H. Sebastian Seung (J. Parallel Distributed Comput., 2017)

Program Synthesis Through Reinforcement Learning Guided Tree Search. - Riley Simmons-Edler; Anders Miltner; H. Sebastian Seung (CoRR, 2018)

Unsupervised learning by a nonlinear network with Hebbian excitatory and anti-Hebbian inhibitory neurons. - H. Sebastian Seung (CoRR, 2018)

Two "correlation games" for a nonlinear network with Hebbian excitatory neurons and anti-Hebbian inhibitory neurons. - H. Sebastian Seung (CoRR, 2018)

Unsupervised learning by a "softened" correlation game: duality and convergence. - Kyle L. Luther; Runzhe Yang; H. Sebastian Seung (ACSSC, 2019)

Weakly Supervised Deep Metric Learning for Template Matching. - Davit Buniatyan; Sergiy Popovych; Dodam Ih; Thomas Macrina; Jonathan Zung; H. Sebastian Seung (CVC (1), 2019)

PZnet: Efficient 3D ConvNet Inference on Manycore CPUs. - Sergiy Popovych; Davit Buniatyan; Aleksandar Zlateski; Kai Li 001; H. Sebastian Seung (CVC (1), 2019)

Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner. - Tarik Tosun; Eric Mitchell; Ben Eisner; Jinwook Huh; Bhoram Lee; Daewon Lee; Volkan Isler; H. Sebastian Seung; Daniel D. Lee (IROS, 2019)

Reconstructing Neuronal Anatomy from Whole-Brain Images. - James Gornet; Kannan Umadevi Venkataraju; Arun Narasimhan; Nicholas L. Turner; Kisuk Lee; H. Sebastian Seung; Pavel Osten; Uygar Sümbül (ISBI, 2019)

Learning Metric Graphs for Neuron Segmentation in Electron Microscopy Images. - Kyle Luther; H. Sebastian Seung (ISBI, 2019)

Variance-Preserving Initialization Schemes Improve Deep Network Training: But Which Variance is Preserved? - Kyle Luther; H. Sebastian Seung (CoRR, 2019)

Convergence of gradient descent-ascent analyzed as a Newtonian dynamical system with dissipation. - H. Sebastian Seung (CoRR, 2019)

Q-Learning for Continuous Actions with Cross-Entropy Guided Policies. - Riley Simmons-Edler; Ben Eisner; Eric Mitchell; H. Sebastian Seung; Daniel D. Lee (CoRR, 2019)

Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision. - Eric Mitchell; Stefan Keselj; Sergiy Popovych; Davit Buniatyan; H. Sebastian Seung (CoRR, 2019)

Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets. - Jingpeng Wu; William M. Silversmith; H. Sebastian Seung (CoRR, 2019)

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy. - Kisuk Lee; Nicholas L. Turner; Thomas Macrina; Jingpeng Wu; Ran Lu; H. Sebastian Seung (CoRR, 2019)

QXplore: Q-learning Exploration by Maximizing Temporal Difference Error. - Riley Simmons-Edler; Ben Eisner; Eric Mitchell; H. Sebastian Seung; Daniel D. Lee (CoRR, 2019)

Learning Dense Voxel Embeddings for 3D Neuron Reconstruction. - Kisuk Lee; Ran Lu; Kyle Luther; H. Sebastian Seung (CoRR, 2019)

Reward Prediction Error as an Exploration Objective in Deep RL. - Riley Simmons-Edler; Ben Eisner; Daniel Yang; Anthony Bisulco; Eric Mitchell; H. Sebastian Seung; Daniel D. Lee (IJCAI, 2020)

Acoustic Collision Detection and Localization for Robot Manipulators. - Xiaoran Fan; Daewon Lee; Yuan Chen 006; Colin Prepscius; Volkan Isler; Larry D. Jackel; H. Sebastian Seung; Daniel D. Lee (IROS, 2020)

Caesar: Segment-Wise Alignment Method for Solving Discontinuous Deformations. - Sergiy Popovych; J. Alexander Bae; H. Sebastian Seung (ISBI, 2020)

Synaptic Partner Assignment Using Attentional Voxel Association Networks. - Nicholas L. Turner; Kisuk Lee; Ran Lu; Jingpeng Wu; Dodam Ih; H. Sebastian Seung (ISBI, 2020)

Large-scale image segmentation based on distributed clustering algorithms. - Ran Lu; Aleksandar Zlateski; H. Sebastian Seung (CoRR, 2021)

Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction. - Kisuk Lee; Ran Lu; Kyle Luther; H. Sebastian Seung (IEEE Trans. Medical Imaging, 2021)

Kernel similarity matching with Hebbian networks. - Kyle Luther; H. Sebastian Seung (NeurIPS, 2022)

Kernel similarity matching with Hebbian neural networks. - Kyle Luther; H. Sebastian Seung (CoRR, 2022)

Stacked unsupervised learning with a network architecture found by supervised meta-learning. - Kyle Luther; H. Sebastian Seung (CoRR, 2022)

RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations. - Jingpeng Wu; Nicholas L. Turner; J. Alexander Bae; Ashwin Vishwanathan; H. Sebastian Seung (Frontiers Neuroinformatics, 2022)

Sensitivity of Sparse Codes to Image Distortions. - Kyle Luther; H. Sebastian Seung (Neural Comput., 2022)

Stretched sinograms for limited-angle tomographic reconstruction with neural networks. - Kyle L. Luther; H. Sebastian Seung (CoRR, 2023)

DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization. - Kyle Luther; H. Sebastian Seung (CoRR, 2023)