Publication

Found 912 results
Author Title [ Type(Asc)] Year
Dataset
Ma, K. - T., Sim, T. & Kankanhalli, M. VIP: A unifying framework for eye-gaze research. (2013). at <http://mmas.comp.nus.edu.sg/VIP.html>
Berzak, Y. et al. Treebank of Learner English (TLE). (2016). at <http://esltreebank.org/>PDF icon acl2016.pdf (163.86 KB)
Liu, H., Agam, Y., Madsen, J. & Kreiman, G. Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex. (2009). at <http://klab.tch.harvard.edu/resources/liuetal_timing3.html>
Miconi, T., Groomes, L. & Kreiman, G. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]. (2016).
Garrote, E. et al. System for Mouse Behavior Recognition. (2010).
Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of unconstrained face recognition. (2014).
Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>
Xu, J., Jiang, M., Wang, S., Kankanhalli, M. & Zhao, Q. Predicting Saliency Beyond Pixels. (2014). at <http://www.ece.nus.edu.sg/stfpage/eleqiz/predicting.html>
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Kreiman, G. People, objects and interactions in movies. (2014).
Rosasco, L. Object recognition data sets (iCub/IIT). (2013).
Isik, L. & Tacchetti, A. MEG action recognition data. (2018). doi:https://doi.org/10.7910/DVN/KFYY2M
Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T. A Large Video Database for Human Motion Recognition. (2011).PDF icon Kuehne_etal_ICCV2011.pdf (433.27 KB)
Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Language and Vision Ambiguities (LAVA) Corpus. (2016). at <http://web.mit.edu/lavacorpus/>PDF icon D15-1172.pdf (2.42 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant action recognition dataset. (2017). at <https://doi.org/10.7910/DVN/DMT0PG>
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).Binary Data modularity_dataset_ver1.tar.gz (36.14 MB)
Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. (2014). doi:http://dx.doi.org/10.7910/DVN/KRUPXZ
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Conference Proceedings
Ellis, K. et al. Write, Execute, Assess: Program Synthesis with a REPL. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9116-write-execute-assess-program-synthesis-with-a-repl.pdf (3.9 MB)
Bomatter, P. et al. When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) (2021). doi:10.1109/iccv48922.2021.00032PDF icon Bomatter_When_Pigs_Fly_Contextual_Reasoning_in_Synthetic_and_Natural_Scenes_ICCV_2021_paper.pdf (3.24 MB)
Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Han, C., Mao, J., Gan, C., Tenenbaum, J. B. & Wu, J. Visual Concept-Metaconcept Learning. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 8745-visual-concept-metaconcept-learning.pdf (1.92 MB)
Stephenson, C. et al. Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)
Anselmi, F. et al. Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013).PDF icon 1311.4158v2.pdf (3.78 MB)

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