Daniel D. Lee

发表

Learning the parts of objects by non-negative matrix factorization

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 1999, Nature.

Unsupervised Learning by Convex and Conic Coding

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 1996, NIPS.

The Manifold Ways of Perception

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 2000, Science.

A Neural Network Based Head Tracking System

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 1997, NIPS.

The Rectified Gaussian Distribution

H. Sebastian Seung, Daniel D. Lee, Nicholas D. Socci, 1997, NIPS.

Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons

Daniel D. Lee, H. Seung, D. Tank, 2000, Neuron.

Learning in Intelligent Embedded Systems

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 1999, USENIX Workshop on Embedded Systems.

Algorithms for Non-negative Matrix Factorization

H. Sebastian Seung, Daniel D. Lee, Daniel D. Lee, 2000, NIPS.

An Information Maximization Approach to Overcomplete and Recurrent Representations

Daniel D. Lee, Oren Shriki, Haim Sompolinsky, 2000, NIPS.

Short-term memory in orthogonal neural networks.

Haim Sompolinsky, Daniel D Lee, Olivia L White, 2004, Physical review letters.

The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback

H. Sebastian Seung, Daniel D. Lee, David W. Tank, 2004, Journal of Computational Neuroscience.

Nonlinear network models of the oculomotor integrator

Daniel D. Lee, H. Seung, D. Tank, 1997 .

A theory of learning with constrained weight-distribution

Daniel D. Lee, H. Sompolinsky, Ben Sorscher, 2022, ArXiv.

Short-term memory in orthogonal neural networks.

Daniel D. Lee, H. Sompolinsky, O. White, 2004, Physical review letters.

A theory of learning with constrained weight-distribution

Daniel D. Lee, H. Sompolinsky, Ben Sorscher, 2022, ArXiv.

Reward Prediction Error as an Exploration Objective in Deep RL

H. Sebastian Seung, Daniel D. Lee, Eric Mitchell, 2019, IJCAI.

Learning Data Manifolds with a Cutting Plane Method

Daniel D. Lee, Haim Sompolinsky, Uri Cohen, 2017, Neural Computation.

Algorithms for Independent Components Analysis and Higher Order Statistics

Daniel D. Lee, Haim Sompolinsky, Uri Rokni, 1999, NIPS.

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Learning a Continuous Hidden Variable Model for Binary Data

Daniel D. Lee, Haim Sompolinsky, Daniel D. Lee, 1998, NIPS.

Separability and geometry of object manifolds in deep neural networks

Haim Sompolinsky, Uri Cohen, Sueyeon Chung, 2019, Nature Communications.

Classification and Geometry of General Perceptual Manifolds pdf

Daniel D. Lee, Haim Sompolinsky, SueYeon Chung, 2017, Physical Review X.

Biologically inspired computation and learning in Sensorimotor Systems

Daniel D. Lee, H. Sabastian Seung, Daniel D. Lee, 2001, SPIE Optics + Photonics.

Linear readout of object manifolds.

Haim Sompolinsky, Daniel D Lee, SueYeon Chung, 2015, Physical review. E.

Equilibrium properties of temporally asymmetric Hebbian plasticity.

Daniel D. Lee, H. Sompolinsky, J. Rubin, 2000, Physical review letters.

The Nonnegative Boltzmann Machine

Daniel D. Lee, David J. C. MacKay, Oliver B. Downs, 1999, NIPS.

The Advanced Computing Systems Association Learning in Intelligent Embedded Systems Learning in Intelligent Embedded Systems

Daniel D. Lee, H. Seung, 2022 .