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2020 – today
- 2023
[i32]Kyle L. Luther, H. Sebastian Seung:
Stretched sinograms for limited-angle tomographic reconstruction with neural networks. CoRR abs/2306.10201 (2023)- [i31]Kyle Luther, H. Sebastian Seung:
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization. CoRR abs/2307.04946 (2023) - 2022
- [j19]Jingpeng Wu, Nicholas L. Turner, J. Alexander Bae, Ashwin Vishwanathan, H. Sebastian Seung:
RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations. Frontiers Neuroinformatics 16: 828169 (2022) - [j18]Kyle Luther, H. Sebastian Seung:
Sensitivity of Sparse Codes to Image Distortions. Neural Comput. 34(7): 1616-1635 (2022) - [c44]Kyle Luther, H. Sebastian Seung:
Kernel similarity matching with Hebbian networks. NeurIPS 2022 - [i30]Kyle Luther, H. Sebastian Seung:
Sensitivity of sparse codes to image distortions. CoRR abs/2204.07466 (2022) - [i29]Kyle Luther, H. Sebastian Seung:
Kernel similarity matching with Hebbian neural networks. CoRR abs/2204.07475 (2022) - [i28]Kyle Luther, H. Sebastian Seung:
Stacked unsupervised learning with a network architecture found by supervised meta-learning. CoRR abs/2206.02716 (2022) - 2021
- [j17]Kisuk Lee
, Ran Lu, Kyle Luther, H. Sebastian Seung:
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction. IEEE Trans. Medical Imaging 40(12): 3801-3811 (2021) - [i27]Ran Lu, Aleksandar Zlateski, H. Sebastian Seung:
Large-scale image segmentation based on distributed clustering algorithms. CoRR abs/2106.10795 (2021) - 2020
- [c43]Riley Simmons-Edler, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, H. Sebastian Seung, Daniel D. Lee:
Reward Prediction Error as an Exploration Objective in Deep RL. IJCAI 2020: 2816-2823 - [c42]Xiaoran Fan, Daewon Lee, Yuan Chen, Colin Prepscius, Volkan Isler, Larry D. Jackel, H. Sebastian Seung, Daniel D. Lee:
Acoustic Collision Detection and Localization for Robot Manipulators. IROS 2020: 9529-9536 - [c41]Nicholas L. Turner, Kisuk Lee, Ran Lu, Jingpeng Wu, Dodam Ih, H. Sebastian Seung:
Synaptic Partner Assignment Using Attentional Voxel Association Networks. ISBI 2020: 1-5 - [c40]Sergiy Popovych, J. Alexander Bae, H. Sebastian Seung:
Caesar: Segment-Wise Alignment Method for Solving Discontinuous Deformations. ISBI 2020: 1214-1218
2010 – 2019
- 2019
- [c39]Kyle L. Luther, Runzhe Yang, H. Sebastian Seung:
Unsupervised learning by a "softened" correlation game: duality and convergence. ACSSC 2019: 876-883 - [c38]Davit Buniatyan, Sergiy Popovych, Dodam Ih, Thomas Macrina, Jonathan Zung, H. Sebastian Seung:
Weakly Supervised Deep Metric Learning for Template Matching. CVC (1) 2019: 39-58 - [c37]Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski, Kai Li, H. Sebastian Seung:
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs. CVC (1) 2019: 369-383 - [c36]Tarik Tosun, Eric Mitchell, Ben Eisner, Jinwook Huh, Bhoram Lee, Daewon Lee, Volkan Isler, H. Sebastian Seung, Daniel D. Lee:
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner. IROS 2019: 7431-7438 - [c35]James Gornet, Kannan Umadevi Venkataraju, Arun Narasimhan, Nicholas L. Turner, Kisuk Lee, H. Sebastian Seung, Pavel Osten, Uygar Sümbül:
Reconstructing Neuronal Anatomy from Whole-Brain Images. ISBI 2019: 218-222 - [c34]Kyle Luther, H. Sebastian Seung:
Learning Metric Graphs for Neuron Segmentation in Electron Microscopy Images. ISBI 2019: 244-248 - [i26]Kyle Luther, H. Sebastian Seung:
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images. CoRR abs/1902.00100 (2019) - [i25]Kyle Luther, H. Sebastian Seung:
Variance-Preserving Initialization Schemes Improve Deep Network Training: But Which Variance is Preserved? CoRR abs/1902.04942 (2019) - [i24]H. Sebastian Seung:
Convergence of gradient descent-ascent analyzed as a Newtonian dynamical system with dissipation. CoRR abs/1903.02536 (2019) - [i23]James Gornet, Kannan Umadevi Venkataraju, Arun Narasimhan, Nicholas L. Turner, Kisuk Lee, H. Sebastian Seung, Pavel Osten, Uygar Sümbül:
Reconstructing neuronal anatomy from whole-brain images. CoRR abs/1903.07027 (2019) - [i22]Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski, Kai Li, H. Sebastian Seung:
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs. CoRR abs/1903.07525 (2019) - [i21]Riley Simmons-Edler, Ben Eisner, Eric Mitchell, H. Sebastian Seung, Daniel D. Lee:
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies. CoRR abs/1903.10605 (2019) - [i20]Eric Mitchell, Stefan Keselj, Sergiy Popovych, Davit Buniatyan, H. Sebastian Seung:
Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision. CoRR abs/1904.02643 (2019) - [i19]Tarik Tosun, Eric Mitchell, Ben Eisner, Jinwook Huh, Bhoram Lee, Daewon Lee, Volkan Isler, H. Sebastian Seung, Daniel D. Lee:
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner. CoRR abs/1904.03260 (2019) - [i18]Nicholas L. Turner, Kisuk Lee, Ran Lu, Jingpeng Wu, Dodam Ih, H. Sebastian Seung:
Synaptic Partner Assignment Using Attentional Voxel Association Networks. CoRR abs/1904.09947 (2019) - [i17]Jingpeng Wu, William M. Silversmith, H. Sebastian Seung:
Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets. CoRR abs/1904.10489 (2019) - [i16]Kisuk Lee, Nicholas L. Turner, Thomas Macrina, Jingpeng Wu, Ran Lu, H. Sebastian Seung:
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy. CoRR abs/1904.12966 (2019) - [i15]Riley Simmons-Edler, Ben Eisner, Eric Mitchell, H. Sebastian Seung, Daniel D. Lee:
QXplore: Q-learning Exploration by Maximizing Temporal Difference Error. CoRR abs/1906.08189 (2019) - [i14]Kisuk Lee, Ran Lu, Kyle Luther, H. Sebastian Seung:
Learning Dense Voxel Embeddings for 3D Neuron Reconstruction. CoRR abs/1909.09872 (2019) - 2018
- [i13]Riley Simmons-Edler, Anders Miltner, H. Sebastian Seung:
Program Synthesis Through Reinforcement Learning Guided Tree Search. CoRR abs/1806.02932 (2018) - [i12]H. Sebastian Seung:
Unsupervised learning by a nonlinear network with Hebbian excitatory and anti-Hebbian inhibitory neurons. CoRR abs/1812.11581 (2018) - [i11]H. Sebastian Seung:
Two "correlation games" for a nonlinear network with Hebbian excitatory neurons and anti-Hebbian inhibitory neurons. CoRR abs/1812.11937 (2018) - 2017
- [j16]Ignacio Arganda-Carreras, Verena Kaynig, Curtis Rueden, Kevin W. Eliceiri, Johannes E. Schindelin, Albert Cardona, H. Sebastian Seung:
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinform. 33(15): 2424-2426 (2017) - [j15]Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung:
Scalable training of 3D convolutional networks on multi- and many-cores. J. Parallel Distributed Comput. 106: 195-204 (2017) - [c33]Aleksandar Zlateski, H. Sebastian Seung:
Compile-time optimized and statically scheduled N-D convnet primitives for multi-core and many-core (Xeon Phi) CPUs. ICS 2017: 8:1-8:10 - [c32]Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung:
An Error Detection and Correction Framework for Connectomics. NIPS 2017: 6818-6829 - [i10]H. Sebastian Seung, Jonathan Zung:
A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination. CoRR abs/1704.00646 (2017) - [i9]Davit Buniatyan, Thomas Macrina, Dodam Ih, Jonathan Zung, H. Sebastian Seung:
Deep Learning Improves Template Matching by Normalized Cross Correlation. CoRR abs/1705.08593 (2017) - [i8]Kisuk Lee, Jonathan Zung, Peter Li, Viren Jain, H. Sebastian Seung:
Superhuman Accuracy on the SNEMI3D Connectomics Challenge. CoRR abs/1706.00120 (2017) - [i7]Jonathan Zung, Ignacio Tartavull, H. Sebastian Seung:
An Error Detection and Correction Framework for Connectomics. CoRR abs/1708.02599 (2017) - 2016
- [c31]Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung:
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-core and Many-Core Shared Memory Machines. IPDPS 2016: 801-811 - [c30]Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung:
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks. NIPS 2016: 3270-3278 - [c29]Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung:
ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs. SC 2016: 854-865 - [i6]Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung:
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs. CoRR abs/1606.05688 (2016) - [i5]Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung:
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks. CoRR abs/1606.07372 (2016) - 2015
- [c28]Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, H. Sebastian Seung:
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction. NIPS 2015: 3573-3581 - [i4]Aleksandar Zlateski, H. Sebastian Seung:
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph. CoRR abs/1505.00249 (2015) - [i3]Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, H. Sebastian Seung:
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection. CoRR abs/1508.04843 (2015) - [i2]Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung:
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines. CoRR abs/1510.06706 (2015) - 2011
- [c27]Viren Jain, Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Learning to Agglomerate Superpixel Hierarchies. NIPS 2011: 648-656 - 2010
- [j14]Srinivas C. Turaga, Joseph F. Murray, Viren Jain, Fabian Roth, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, H. Sebastian Seung:
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation. Neural Comput. 22(2): 511-538 (2010) - [c26]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, Ken J. Hayworth, Richard Schalek, Juan Carlos Tapia, Jeff W. Lichtman, H. Sebastian Seung:
Boundary Learning by Optimization with Topological Constraints. CVPR 2010: 2488-2495
2000 – 2009
- 2009
- [j13]Yonatan Loewenstein, Drazen Prelec, H. Sebastian Seung:
Operant Matching as a Nash Equilibrium of an Intertemporal Game. Neural Comput. 21(10): 2755-2773 (2009) - [c25]Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Maximin affinity learning of image segmentation. NIPS 2009: 1865-1873 - [i1]Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Maximin affinity learning of image segmentation. CoRR abs/0911.5372 (2009) - 2008
- [c24]Viren Jain, H. Sebastian Seung:
Natural Image Denoising with Convolutional Networks. NIPS 2008: 769-776 - 2007
- [j12]Dezhe Z. Jin, Fethi M. Ramazanoglu, H. Sebastian Seung:
Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC. J. Comput. Neurosci. 23(3): 283-299 (2007) - [c23]Viren Jain, Joseph F. Murray, Fabian Roth, Srinivas C. Turaga, Valentin P. Zhigulin, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Supervised Learning of Image Restoration with Convolutional Networks. ICCV 2007: 1-8 - 2006
- [j11]Whitman Richards, H. Sebastian Seung, Galen Pickard:
Neural voting machines. Neural Networks 19(8): 1161-1167 (2006) - 2005
- [j10]Justin Werfel, Xiaohui Xie, H. Sebastian Seung:
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks. Neural Comput. 17(12): 2699-2718 (2005) - [c22]Viren Jain, Valentin P. Zhigulin, H. Sebastian Seung:
Representing Part-Whole Relationships in Recurrent Neural Networks. NIPS 2005: 563-570 - 2004
- [j9]Brett D. Mensh, Justin Werfel, H. Sebastian Seung:
BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Trans. Biomed. Eng. 51(6): 1052-1056 (2004) - [c21]Russ Tedrake, Teresa Weirui Zhang, Ming-fai Fong, H. Sebastian Seung:
Actuating a Simple 3D Passive Dynamic Walker. ICRA 2004: 4656-4661 - [c20]Russ Tedrake, Teresa Weirui Zhang, H. Sebastian Seung:
Stochastic policy gradient reinforcement learning on a simple 3D biped. IROS 2004: 2849-2854 - 2003
- [j8]Xiaohui Xie, H. Sebastian Seung:
Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network. Neural Comput. 15(2): 441-454 (2003) - [j7]Richard H. R. Hahnloser, H. Sebastian Seung, Jean-Jacques E. Slotine:
Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. Neural Comput. 15(3): 621-638 (2003) - [c19]Justin Werfel, Xiaohui Xie, H. Sebastian Seung:
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks. NIPS 2003: 1197-1204 - 2002
- [j6]Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian Seung:
Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition. Neural Comput. 14(11): 2627-2646 (2002) - 2001
- [c18]Richard H. R. Hahnloser, Xiaohui Xie, H. Sebastian Seung:
A theory of neural integration in the head-direction system. NIPS 2001: 221-228 - 2000
- [j5]H. Sebastian Seung, Daniel D. Lee, Ben Y. Reis, David W. Tank:
The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback. J. Comput. Neurosci. 9(2): 171-185 (2000) - [c17]Richard H. R. Hahnloser, H. Sebastian Seung:
Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. NIPS 2000: 217-223 - [c16]Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian Seung:
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks. NIPS 2000: 350-356 - [c15]Daniel D. Lee, H. Sebastian Seung:
Algorithms for Non-negative Matrix Factorization. NIPS 2000: 556-562
1990 – 1999
- 1999
- [c14]Daniel D. Lee, H. Sebastian Seung:
Learning in Intelligent Embedded Systems. USENIX Workshop on Embedded Systems 1999 - [c13]Xiaohui Xie, H. Sebastian Seung:
Spike-based Learning Rules and Stabilization of Persistent Neural Activity. NIPS 1999: 199-208 - 1998
- [j4]H. Sebastian Seung:
Continuous attractors and oculomotor control. Neural Networks 11(7-8): 1253-1258 (1998) - 1997
- [j3]Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby:
Selective Sampling Using the Query by Committee Algorithm. Mach. Learn. 28(2-3): 133-168 (1997) - [c12]H. Sebastian Seung, Tom J. Richardson, J. C. Lagarias, John J. Hopfield:
Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks. NIPS 1997: 329-335 - [c11]Nicholas D. Socci, Daniel D. Lee, H. Sebastian Seung:
The Rectified Gaussian Distribution. NIPS 1997: 350-356 - [c10]Jong-Hoon Oh, H. Sebastian Seung:
Learning Generative Models with the Up-Propagation Algorithm. NIPS 1997: 605-611 - [c9]H. Sebastian Seung:
Learning Continuous Attractors in Recurrent Networks. NIPS 1997: 654-660 - [c8]Daniel D. Lee, H. Sebastian Seung:
A Neural Network Based Head Tracking System. NIPS 1997: 908-914 - 1996
- [j2]David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby:
Rigorous Learning Curve Bounds from Statistical Mechanics. Mach. Learn. 25(2-3): 195-236 (1996) - [c7]Daniel D. Lee, H. Sebastian Seung:
Unsupervised Learning by Convex and Conic Coding. NIPS 1996: 515-521 - 1995
- [j1]Michael J. Kearns, H. Sebastian Seung:
Learning from a Population of Hypotheses. Mach. Learn. 18(2-3): 255-276 (1995) - 1994
- [c6]David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby:
Rigorous Learning Curve Bounds from Statistical Mechanics. COLT 1994: 76-87 - [c5]N. Barkai, H. Sebastian Seung, Haim Sompolinsky:
On-line Learning of Dichotomies. NIPS 1994: 303-310 - 1993
- [c4]Michael J. Kearns, H. Sebastian Seung:
Learning from a Population of Hypotheses. COLT 1993: 101-110 - 1992
- [c3]H. Sebastian Seung, Manfred Opper, Haim Sompolinsky:
Query by Committee. COLT 1992: 287-294 - [c2]Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby:
Information, Prediction, and Query by Committee. NIPS 1992: 483-490 - 1991
- [c1]H. Sebastian Seung, Haim Sompolinsky, Naftali Tishby:
Learning Curves in Large Neural Networks. COLT 1991: 112-127
Coauthor Index
Kyle Luther
aka: Kyle L. Luther
aka: Kyle L. Luther
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