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On the concrete hardness of Learning with Errors
"... Abstract. The Learning with Errors (LWE) problem has become a central building block of modern cryptographic constructions. This work collects and presents hardness results for concrete instances of LWE. In particular, we discuss algorithms proposed in the literature and give the expected resources ..."
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Cited by 1 (0 self)
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Abstract. The Learning with Errors (LWE) problem has become a central building block of modern cryptographic constructions. This work collects and presents hardness results for concrete instances of LWE. In particular, we discuss algorithms proposed in the literature and give the expected resources
On Lattices, Learning with Errors, Random Linear Codes, and Cryptography
 In STOC
, 2005
"... Our main result is a reduction from worstcase lattice problems such as SVP and SIVP to a certain learning problem. This learning problem is a natural extension of the ‘learning from parity with error’ problem to higher moduli. It can also be viewed as the problem of decoding from a random linear co ..."
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Cited by 364 (6 self)
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Our main result is a reduction from worstcase lattice problems such as SVP and SIVP to a certain learning problem. This learning problem is a natural extension of the ‘learning from parity with error’ problem to higher moduli. It can also be viewed as the problem of decoding from a random linear
Soft Margins for AdaBoost
, 1998
"... Recently ensemble methods like AdaBoost were successfully applied to character recognition tasks, seemingly defying the problems of overfitting. This paper shows that although AdaBoost rarely overfits in the low noise regime it clearly does so for higher noise levels. Central for understanding this ..."
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Cited by 333 (24 self)
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this fact is the margin distribution and we find that AdaBoost achieves  doing gradient descent in an error function with respect to the margin  asymptotically a hard margin distribution, i.e. the algorithm concentrates its resources on a few hardtolearn patterns (here an interesting overlap emerge
Classical hardness of Learning with Errors
, 2013
"... We show that the Learning with Errors (LWE) problem is classically at least as hard as standard worstcase lattice problems, even with polynomial modulus. Previously this was only known under quantum reductions. Our techniques capture the tradeoff between the dimension and the modulus of LWE instanc ..."
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Cited by 43 (12 self)
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We show that the Learning with Errors (LWE) problem is classically at least as hard as standard worstcase lattice problems, even with polynomial modulus. Previously this was only known under quantum reductions. Our techniques capture the tradeoff between the dimension and the modulus of LWE
What is the Best MultiStage Architecture for Object Recognition?
"... In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a nonlinear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hardwired, or two stages where the filter ..."
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Cited by 252 (22 self)
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In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a nonlinear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hardwired, or two stages where
The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations
, 1993
"... We prove the following about the Nearest Lattice Vector Problem (in any `p norm), the Nearest Codeword Problem for binary codes, the problem of learning a halfspace in the presence of errors, and some other problems. 1. Approximating the optimum within any constant factor is NPhard. 2. If for some ..."
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Cited by 170 (7 self)
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We prove the following about the Nearest Lattice Vector Problem (in any `p norm), the Nearest Codeword Problem for binary codes, the problem of learning a halfspace in the presence of errors, and some other problems. 1. Approximating the optimum within any constant factor is NPhard. 2
On ideal lattices and learning with errors over rings
 In Proc. of EUROCRYPT, volume 6110 of LNCS
, 2010
"... The “learning with errors ” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worstcase lattice problems, and in recent years it has served as the foundation for a pleth ..."
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Cited by 125 (18 self)
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The “learning with errors ” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worstcase lattice problems, and in recent years it has served as the foundation for a
Machine recognition of human activities: A survey
, 2008
"... The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as contentbased video annotation and retrieval, highlight extraction and video summarization require recognition of the a ..."
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Cited by 218 (0 self)
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of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing—robustness against errors in lowlevel processing, view
Deep Dyslexia: A Case Study of Connectionist Neuropsychology
, 1993
"... Deep dyslexia is an acquired reading disorder marked by the occurrence of semantic errors (e.g., reading RIVER as "ocean"). In addition, patients exhibit a number of other symptoms, including visual and morphological effects in their errors, a partofspeech effect, and an advantage for co ..."
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Cited by 196 (29 self)
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for concrete over abstract words. Deep dyslexia poses a distinct challenge for cognitive neuropsychology because there is little understanding of why such a variety of symptoms should cooccur in virtually all known patients. Hinton and Shallice (1991) replicated the cooccurrence of visual and semantic errors
Results 1  10
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