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Highly Cited

2018

Highly Cited

2018

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to… Expand

Highly Cited

2007

Highly Cited

2007

One of the major settings of global sensitivity analysis is that of fixing non-influential factors, in order to reduce the… Expand

Highly Cited

2006

Highly Cited

2006

Several results appeared that show significant reduction in time for matrix multiplication, singular value decomposition as well… Expand

Highly Cited

2003

Highly Cited

2003

Let B be a Banach space and (ℋ,‖·‖ℋ) be a dense, imbedded subspace. For a ∈ B, its distance to the ball of ℋ with radius R… Expand

Highly Cited

2001

Highly Cited

2001

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than… Expand

Highly Cited

2000

Highly Cited

2000

This monograph presents a summary account of the subject of a posteriori error estimation for finite element approximations of… Expand

Highly Cited

1998

Highly Cited

1998

This paper presents a new tool, Metro, designed to compensate for a deficiency in many simplification methods proposed in… Expand

Highly Cited

1997

Highly Cited

1997

Abstract In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset… Expand

Highly Cited

1996

Highly Cited

1996

We present new results about the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of a… Expand

Highly Cited

1993

Highly Cited

1993

Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one… Expand