Results 21 to 30 of about 13,863,270 (332)
Directly Fine-Tuning Diffusion Models on Differentiable Rewards [PDF]
International Conference on Learning Representations, 2023 We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models.
Kevin Clark +3 more
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E-B-invexity in E-differentiable mathematical programming
Results in Control and Optimization, 2021 In this paper, a new concept of generalized convexity is introduced for (not necessarily) differentiable optimization problem with E-differentiable functions. Namely, for an E-differentiable function, the concept of E-B-invexity is defined.
Najeeb Abdulaleem
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Composite differentiable functions [PDF]
Duke Mathematical Journal, 1996 19 pages, hard copy available on request.
Bierstone, Edward +2 more
openaire +4 more sources
Intermittency of Riemann’s non-differentiable function through the fourth-order flatness [PDF]
Journal of Mathematics and Physics, 2019 Riemann’s non-differentiable function is one of the most famous examples of continuous but nowhere differentiable functions, but it has also been shown to be relevant from a physical point of view.
Alexandre Boritchev +2 more
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E-invexity and generalized E-invexity in E-differentiable multiobjective programming [PDF]
ITM Web of Conferences, 2019 In this paper, a new concept of generalized convexity is introduced for not necessarily differentiable vector optimization problems. For an E-differentiable function, the concept of E-invexity is introduced as a generalization of the E-differentiable E ...
Abdulaleem Najeeb
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Surveys in Mathematics and its Applications, 2023 We present a streamlined, slightly modified version, in the two-variable situation, of a beautiful, but not so well known, theory by Bögel, already from the 1930s, on an alternative higher dimensional calculus of real functions, a double calculus, which ...
Patrik Lundström
doaj
A New Parameterless Filled Function Method for Global Optimization
Axioms, 2022 The filled function method is an effective way to solve global optimization problems. However, its effectiveness is greatly affected by the selection of parameters, and the non-continuous or non-differentiable properties of the constructed filled ...
Haiyan Liu +3 more
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DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing [PDF]
Computer Vision and Pattern Recognition, 2019 We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.
Shaohui Liu +5 more
semanticscholar +1 more source
Axioms, 2023 Hermite–Hadamard inequality is a double inequality that provides an upper and lower bounds of the mean (integral) of a convex function over a certain interval.
Ibtisam Aldawish +2 more
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