S. Hanson

发表

Advances in Neural Information Processing Systems 30

Michael I. Jordan, Stephen José Hanson, S. Hanson, 1995 .

Mixture Models for Learning from Incomplete Data

S. Hanson, R. Greiner, T. Petsche, 1997 .

Comparing Biases for Minimal Network Construction with Back-Propagation

Lorien Y. Pratt, Stephen Jose Hanson, S. Hanson, 1988, NIPS.

Advances in neural information processing systems : proceedings of the ... conference

Thomas G. Dietterich, Richard P. Lippmann, Klaus-Robert Müller, 1989 .

Nonlinear Autoassociation Is Not Equivalent to PCA

Nathalie Japkowicz, Mark A. Gluck, Stephen Jose Hanson, 2000, Neural Computation.

Spherical Units as Dynamic Consequential Regions: Implications for Attention, Competition and Categorization

S. Hanson, M. Gluck, 1990, NIPS 1990.

PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

Stefan Pollmann, Yaroslav O. Halchenko, Per B. Sederberg, 2009, Neuroinformatics.

Meiosis Networks

Stephen Jose Hanson, S. Hanson, 1989, NIPS.

Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding

S. Hanson, S. Harnad, J. Lubin, 1995 .

A Neural Network Autoassociator for Induction Motor Failure Prediction

Stephen Jose Hanson, Thomas Petsche, Christian J. Darken, 1995, NIPS.

Measurement and modeling of behavior under fixed-interval schedules of reinforcement.

S. Hanson, P. Killeen, 1981 .

Multivariate analysis of Drosophila courtship.

S. Hanson, T. Markow, 1981, Proceedings of the National Academy of Sciences of the United States of America.

A stochastic version of the delta rule

Stephen José Hanson, S. Hanson, 1990 .

Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals

Stephen Jose Hanson, David J. Burr, S. Hanson, 1987, NIPS.

Constraints and prospects

Ronald L. Rivest, Stephen José Hanson, George A. Drastal, 1994 .

Some Solutions to the Missing Feature Problem in Vision

S. Hanson, J. Cowan, C. L. Giles, 1993 .

Computational learning theory and natural learning systems

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Advancing functional connectivity research from association to causation

Daniele Marinazzo, Michael W. Cole, Stephen José Hanson, 2019, Nature Neuroscience.

Computational Learning Theory and Natural Learning

Stephen José Hanson, Jude Shavlik, Systems-Thomas Petsche, 1996 .

Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia

S. Hanson, C. Hanson, D. Mastrovito, 2018, NeuroImage: Clinical.

Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity

C. Glymour, S. Hanson, C. Hanson, 2016, 1605.09282.

Arousal: its genesis and manifestation as response rate.

S. Hanson, P. Killeen, S. Osborne, 1978, Psychological review.

Dynamic changes in the medial temporal lobe during incidental learning of object-location associations.

S. Hanson, L. Reder, A. Manelis, 2012, Cerebral cortex.

Reinforcement without representation

S. Hanson, 1994 .

Conceptual Clustering, Categorization, and Polymorphy

Stephen José Hanson, Malcolm Bauer, S. Hanson, 1989, Machine Learning.

Modeling activation and effective connectivity of VWFA in same script bilinguals

S. Hanson, C. Hanson, Olga Boukrina, 2014, Human brain mapping.

Attentional Bias in Human Category Learning: The Case of Deep Learning

Stephen José Hanson, Leyla Roskan Caglar, Catherine Hanson, 2018, Front. Psychol..

LEARNING UNAMBIGUOUS REDUCED SEQUENCEDESCRIPTIONSIn

J. E. Moody, R. P. Lippman, J. Moody, 1992 .

Real World Applications

S. Hanson, R. Greiner, T. Petsche, 1997 .

To maximize or not to maximize …

S. Hanson, 1981, Behavioral and Brain Sciences.

Dense mode clustering in brain maps B

S. Hanson, Y. Halchenko, C. Hanson, 2007 .

Technology. His Current Research Interests In- Clude Neural Computation, Statistical Learning Theory, and Handwriting Recognition. Kwok and Yeung: Constructive Algorithms for Structure Learning \some Approximation Properties of Projection Pursuit Learning Networks," in Advances in Neural Information

R. S. Schechter, Systems, J. Friedman, 1997 .

Diversity Associated With Reductions in the Achievement Gap: Data Mining the 2010–2011 New Jersey Assessment of Skills and Knowledge (NJ ASK)

S. Hanson, Michael T. Nettles, Stephanie R. Saunders, 2020 .

Knowing This from That : Brain Response to Objects and Events

S. Hanson, C. Hanson, 2005 .

Enhancementand Their Modulation by Cholinergic Effects of Attention and Emotion on Repetition

Annchen R. Knodt, S. Hanson, J. Driver, 2015 .

Brain network connectivity underlying decisions between the "lesser of two evils"

S. Hanson, C. Hanson, Colleen Mills-Finnerty, 2018 .

fMRI BOLD response in high-risk college students (Part 1): during exposure to alcohol, marijuana, polydrug and emotional picture cues.

S. Hanson, M. Bates, C. Hanson, 2010, Alcohol and alcoholism.

Implicit memory for object locations depends on reactivation of encoding‐related brain regions

S. Hanson, C. Hanson, A. Manelis, 2011, Human brain mapping.

Confidence intervals for nonlinear regression: A BASIC program

S. Hanson, 1978 .

Supervised Learning Using Labeled and Unlabeled Examples

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Brains and Minds: On the Usefulness of Localization Data to Cognitive Psychology

S. Hanson, Richard Loosemore, Trevor A. Harley, 2010 .

Modeling Causal Relationships among Brain Areas in the Mesocorticolimbic System during Resting-State in Cocaine Users Utilizing a Graph Theoretic Approach

S. Hanson, B. Biswal, S. Gohel, 2017, Journal of alcoholism and drug dependence.

Scale-Free Exponents of Resting State are Biomarkers of Neuro-Typical and Atypical Brain Activity

C. Glymour, S. Hanson, C. Hanson, 2016, bioRxiv.

Effective Connectivity of Brain Networks Responsible for Impaired Joint Attention in Individuals with Autism

C. Glymour, S. Hanson, C. Hanson, 2012 .

Scaling to Domains with Irrelevant Features

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

AN AUTOASSOCIATOR FOR ON-LINE MOTOR MONITORING

Stephen José Hanson, Thomas Petsche, Gary M. Kuhn, 1998 .

Back to the future: The return of cognitive functionalism

S. Hanson, L. Çağlar, 2017, Behavioral and Brain Sciences.

Computational learning theory and natural learning systems: Volume IV: making learning systems practical

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997, COLT 1997.

Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Machine Learning: From Theory to Applications

Ronald L. Rivest, Stephen José Hanson, Werner Remmele, 1993, Lecture Notes in Computer Science.

Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment

Ronald L. Rivest, Stephen José Hanson, Thomas Petsche, 1994 .

Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects

Ronald L. Rivest, Stephen José Hanson, George A. Drastal, 1994, Annual Conference Computational Learning Theory.

Strategic Directions in Machine Learning

Ronald L. Rivest, Stephen Jose Hanson, Werner Remmele, 1993, Machine Learning: From Theory to Applications.

Proceedings of the 4th International Conference on Neural Information Processing Systems

Richard P. Lippmann, John E. Moody, Stephen José Hanson, 1991 .

NNES: A Neural Network Explanation System for Transforming Trained Neural Networks into Decision Trees

J. Moody, S. Hanson, 1995 .

On the obvious treatment of connectionism

Stephen José Hanson, S. Hanson, 1988, Behavioral and Brain Sciences.

The RUMBA software

Giorgio Grasso, Stephen Jose Hanson, Benjamin Martin Bly, 2007, Neuroinformatics.

Categorization in Neuroscience: Brain Response to Objects and Events

Stephen José Hanson, Catherine Hanson, S. Hanson, 2005 .

Chapter 9 – Categorization in Neuroscience: Brain Response to Objects and Events

S. Hanson, C. Hanson, 2005 .

Six problems for causal inference from fMRI

Russell A. Poldrack, Clark Glymour, Yaroslav O. Halchenko, 2010, NeuroImage.

Dense mode clustering in brain maps.

Stephen José Hanson, Yaroslav O Halchenko, Catherine Hanson, 2007, Magnetic resonance imaging.

Learning and representation: Tensions at the interface

Steven José Hanson, S. Hanson, 1990, Behavioral and Brain Sciences.

Solving the brain synchrony eigenvalue problem: conservation of temporal dynamics (fMRI) over subjects doing the same task

Stephen Jose Hanson, Catherine Hanson, A. D. Gagliardi, 2008, Journal of Computational Neuroscience.

What connectionist models learn: Learning and representation in connectionist networks

Stephen José Hanson, David J. Burr, S. Hanson, 1990, Behavioral and Brain Sciences.

Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

Stephen José Hanson, Toshihiko Matsuka, James V. Haxby, 2004, NeuroImage.

Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism

Clark Glymour, Stephen Jose Hanson, Catherine Hanson, 2013, Brain Connect..

Representing Categorical Knowledge: A fMRI Study

S. Hanson, T. Yamauchi, T. Matsuka, 2005 .

Using Neural Networks to Modularize Software

Robert W. Schwanke, Stephen José Hanson, S. Hanson, 1994, Machine Learning.

Using neural networks to modularize software

Robert W. Schwanke, Stephen Jose Hanson, S. Hanson, 2004, Machine Learning.

Conceptual clustering, categorization, and polymorphy

Stephen Jose Hanson, Malcolm Bauer, S. Hanson, 2004, Machine Learning.

CONCEPTUAL CLUSTERING AND CATEGORIZATION

Stephen Jose Hanson, S. Hanson, 1990 .

Machine Learning, Clustering and Polymorphy pdf

Stephen Jose Hanson, Malcolm Bauer, S. Hanson, 1985, UAI.

Regulation during challenge: A general model of learned performance under schedule constraint.

Stephen José Hanson, William Timberlake, S. Hanson, 1983 .

Interpreting brain images – reflections on an adolescent field

S. Hanson, M. Bunz, 2010 .

Spherical Units as Dynamic Consequential Regions

Mark A. Gluck, Stephen Jose Hanson, S. Hanson, 1990, NIPS.

High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories

Stephen Jose Hanson, Arielle Schmidt, S. Hanson, 2011, NeuroImage.

Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No Face Identification Area

Yaroslav O. Halchenko, Stephen Jose Hanson, S. Hanson, 2008, Neural Computation.

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study

Clark Glymour, Joseph Ramsey, Stephen Jose Hanson, 2011, NeuroImage.

Latent functional connectivity underlying multiple brain states

Michael W. Cole, S. Hanson, E. McCormick, 2021, bioRxiv.

Foundational issues in human brain mapping

Stephen José Hanson, Martin Bunzl, S. Hanson, 2010 .

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

Stephen José Hanson, Russell A Poldrack, Yaroslav O Halchenko, 2009, Psychological science.

Neuroinformatics Original Research Article Pymvpa: a Unifying Approach to the Analysis of Neuroscientifi C Data

Stefan Pollmann, Yaroslav O. Halchenko, Per B. Sederberg, 2022 .

Modeling Causal Relationship Between Brain Regions Within the Drug-Cue Processing Network in Chronic Cocaine Smokers

Suchismita Ray, Stephen José Hanson, Bharat Biswal, 2015, Neuropsychopharmacology.

Computations and Connectivity Underlying Aversive Counterfactuals

Colleen Mills-Finnerty, Catherine Hanson, Mohannad Khadr, 2020, Brain Connect..

Brain network response underlying decisions about abstract reinforcers

Stephen Jose Hanson, Catherine Hanson, Colleen Mills-Finnerty, 2014, NeuroImage.

Multimodal Integration : fMRI , MRI , EEG , MEG

Barak A. Pearlmutter, Yaroslav O. Halchenko, Stephen José Hanson, 2022 .

The Failure of Blobology: fMRI Misinterpretation, Maleficience and Muddle

S. Hanson, 2022, Frontiers in Human Neuroscience.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

Michael W. Cole, S. Hanson, Takuya Ito, 2023, bioRxiv.

Categorical Perception and the Evolution of Supervised Learning in Neural Nets

Stevan Harnad, S. Hanson, S. Harnad, 1991 .

On the Emergence of Rules in Neural Networks

Stephen Jose Hanson, Michiro Negishi, S. Hanson, 2002, Neural Computation.

Specific neural representation for a conceptual set of behavior: pair bonding

Stephen Jose Hanson, Amanda Maria Dios, Katherine Alexander, 2013 .

Enhanced Cue Reactivity to Cocaine Cues in Non-treatment Seeking Cocaine Smokers

S. Hanson, C. Hanson, Suchismita Ray, 2014 .

fMRI BOLD response of high-risk college students (Part 2): during memory priming of alcohol, marijuana and polydrug picture cues.

S. Hanson, M. Bates, C. Hanson, 2010, Alcohol and alcoholism.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

Michael W. Cole, S. Hanson, Takuya Ito, 2021, NeuroImage.

Programmer perceptions of productivity and programming tools

Richard R. Rosinski, Stephen Jose Hanson, S. Hanson, 1985, CACM.

Central place foraging in Rattus norvegicus

Stephen José Hanson, Peter R. Killeen, S. Hanson, 1981, Animal Behaviour.

The distribution of BOLD susceptibility effects in the brain is non-Gaussian

B M Bly, S. Hanson, B. Bly, 2001, Neuroreport.

Backpropagation: some comments and variations

Stephen José Hanson, S. Hanson, 1995 .

Advances in Neural Information Processing Systems 5, [NIPS Conference]

C. Lee Giles, Jack D. Cowan, Stephen José Hanson, 1992 .

Predicting Structured Data

Gökhan BakIr, Sundar Vishwanathan, Bernhard Schölkopf, 2008 .

What connectionist models learn: Learning and representation in connectionist networks

William Timberlake, S. Hanson, D. Burr, 1990, Behavioral and Brain Sciences.

fMRI BOLD response in high-risk college students (Part 1): during exposure to alcohol, marijuana, polydrug and emotional picture cues.

S. Hanson, M. Bates, C. Hanson, 2010, Alcohol and Alcoholism.

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study

C. Glymour, S. Hanson, J. Ramsey, 2011, NeuroImage.

Six problems for causal inference from fMRI

C. Glymour, S. Hanson, R. Poldrack, 2010, NeuroImage.

Neuroinformatics Original Research Article Pymvpa: a Unifying Approach to the Analysis of Neuroscientifi C Data

S. Hanson, J. Haxby, J. Rieger, 2022 .

Discovering How Brains Do Things

C. Glymour, S. Hanson, 2010 .

Multimodal Integration: fMRI, MRI, EEG, MEG

Barak A. Pearlmutter, S. Hanson, Y. Halchenko, 2005 .

Transmodal Analysis of Neural Signals pdf

Yaroslav O. Halchenko, Michael Hanke, Christoph S. Herrmann, 2013, ArXiv.