Publication
Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (COSYNE) (2018). at <http://www.cosyne.org/c/index.php?title=Cosyne_18>
Learning mid-level codes for natural sounds. Computational and Systems Neuroscience (Cosyne) 2016 (2016). at <http://www.cosyne.org/c/index.php?title=Cosyne2016_posters_2>
Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB)
Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).
Adaptive Coding for Dynamic Sensory Inference. eLife (2018).
Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).
Learning Mid-Level Auditory Codes from Natural Sound Statistics. (2017).
MlynarskiMcDermott_Memo060.pdf (7.11 MB)
Learning Mid-Level Auditory Codes from Natural Sound Statistics. Neural Computation 30, 631-669 (2018).
Learning Mid-Level Codes for Natural Sounds. Association for Otolaryngology Mid-Winter Meeting (2017).
Lossy Compression of Sound Texture by the Human Auditory System. Society for Neuroscience Meeting (2016).
A normalization model of visual search predicts single trial human fixations in an object search task. (2014).
CBMM-Memo-008.pdf (854.51 KB)
There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cerebral Cortex 26(7), 26:3064-3082 (2016).
When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
An analysis of training and generalization errors in shallow and deep networks. (2019).
CBMM-Memo-098.pdf (687.36 KB)
CBMM Memo 098 v4 (08/2019) (2.63 MB)
An analysis of training and generalization errors in shallow and deep networks. (2018).
CBMM-Memo-076.pdf (772.61 KB)
CBMM-Memo-076v2.pdf (2.67 MB)
Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020).
1534-0392_2020_8_4085.pdf (514.57 KB)
Deep vs. shallow networks : An approximation theory perspective. (2016).
Original submission, visit the link above for the updated version (960.27 KB)
Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>
How PFC and LIP process single and multiple-object ‘pop-out’ displays. Society for Neuroscience (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Full%20Abstract%20PDFs%202015/SfN15_Abstracts_PDF_Nanos.ashx>
New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System. The Journal of Neuroscience 35, (2015).
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