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Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-10-19)
We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and ...
Foveation-based Mechanisms Alleviate Adversarial Examples
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-19)
We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in ...
Anchoring and Agreement in Syntactic Annotations
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-09-21)
Published in the Proceedings of EMNLP 2016
We present a study on two key characteristics of human syntactic annotations: anchoring and agreement. Anchoring is a well-known cognitive bias in human decision making, where ...
Fast, invariant representation for human action in the visual system
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-06)
The ability to recognize the actions of others from visual input is essential to humans' daily lives. The neural computations underlying action recognition, however, are still poorly understood. We use magnetoencephalography ...
Where do hypotheses come from?
(Center for Brains, Minds and Machines (CBMM), 2016-10-24)
Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? One notable instance of this discrepancy is that tasks where the candidate hypotheses are explicitly available ...
Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-05)
This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language (ESL) texts. Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate ...
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-03)
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving ...
Probing the compositionality of intuitive functions
(Center for Brains, Minds and Machines (CBMM), 2016-05-26)
How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into ...
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-04-12)
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with ...
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-28)
Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing ...