Publication
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Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018). CBMM-Memo-093.pdf (2.99 MB)
Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
Single-Shot Object Detection with Enriched Semantics. (2018). CBMM-Memo-084.pdf (1.92 MB)
Spatiotemporal interpretation features in the recognition of dynamic images. (2018). CBMM-Memo-094.pdf (1.21 MB) CBMM-Memo-094-dynamic-figures.zip (1.8 MB) fig1.ppsx (147.67 KB) fig2.ppsx (419.72 KB) fig4.ppsx (673.41 KB) figS1.ppsx (587.88 KB) figS2.ppsx (281.56 KB)
A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, (2018).
Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018). 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018). 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
Theory III: Dynamics and Generalization in Deep Networks. (2018). Original, intermediate versions are available under request (2.67 MB) CBMM Memo 90 v12.pdf (4.74 MB) Theory_III_ver44.pdf Update Hessian (4.12 MB) Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB) fixing errors and sharpening some proofs (2.45 MB)
Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 ( ) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf> trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB) NeurIPS2018_Poster.pdf (6.12 MB)
Visual Concepts and Compositional Voting. Annals of Mathematical Sciences and Applications (AMSA) 3, 151–188 (2018).
Visual concepts and compositional voting. (2018). CBMM-Memo-087.pdf (3.37 MB)
What am I searching for?. (2018). CBMM-Memo-096.pdf (1.74 MB)
What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018). Human neurophysiological responses during movies (2.78 MB)
Active Video Summarization: Customized Summaries via On-line Interaction. AAAI Conference on Artificial Intelligence (2017). 21-Garcia-del-Molino-14856.pdf (413.77 KB)
Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).
Auditory Perception of Material and Force from Impact Sounds. Annual Meeting of Association for Research in Otolaryngology (2017).
A Balanced Comparison of Object Invariances in Monkey IT Neurons. eneuro 4, ENEURO.0333-16.2017 (2017).
Causal and compositional generative models in online perception. 39th Annual Conference of the Cognitive Science Society ( ) (2017). yildirim_janner_2_1.pdf (6.88 MB)
Causal and compositional generative models in online perception. 39th Annual Meeting of the Cognitive Science Society - COGSCI 2017 (2017). at <https://mindmodeling.org/cogsci2017/papers/0266/index.html>
Causal learning from interventions and dynamics in continuous time. Cognitive Science Conference (2017). Bramley et al. - 2017 - Causal learning from interventions and dynamics in.pdf (1.78 MB)