In this article, we study the problem of sampling from distributions whose densities are not necessarily smooth nor logconcave. We propose a simple Langevin-based algorithm that does not rely on popular but computatio...
详细信息
We introduce a novel reinforcement learning algorithm (AGRO, for Any-Generation Reward optimization) for fine-tuning large-language models. AGRO leverages the concept of generation consistency, which states that the o...
详细信息
In the rapidly evolving landscape of computer vision and artificial intelligence, transfer learning has emerged as a powerful tool for efficiently applying pre-trained models to new tasks. This article delves into the...
详细信息
In the rapidly evolving landscape of computer vision and artificial intelligence, transfer learning has emerged as a powerful tool for efficiently applying pre-trained models to new tasks. This article delves into the intriguing concept of evolving Convolutional Neural Networks (CNNs) with meta-heuristics for transfer learning in computer vision. The primary focus is on enhancing the adaptability and efficiency of CNNs, making them better suited for specialized tasks. The article covers the significance of transfer learning, the challenges faced in transfer learning with CNNs, the basics of CNN architecture, and the role of meta-heuristics in optimizing CNNs. Real-world applications and success stories demonstrate the transformative potential of these techniques in fields like medical image analysis and autonomous vehicles. It explores emerging trends and potential developments in the domain, emphasizing the impact on various sectors, including healthcare, natural language processing, and robotics. The promise of evolving CNNs with meta-heuristics lies in their capacity to tackle intricate problems with greater precision, ultimately reshaping the landscape of artificial intelligence and machine learning. Ongoing research ensures a promising future for this amalgamation of technologies, promising breakthroughs that will have a lasting impact on the world of computer vision and beyond.
Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent ...
详细信息
We study the PAC property of scenario decision-making algorithms, that is, the ability to make a decision that has an arbitrarily low risk of violating an unknown safety constraint, provided sufficiently many realizat...
详细信息
We propose EAGLE update rule, a novel optimization method that accelerates loss convergence during the early stages of training by leveraging both current and previous step parameter and gradient values. The update al...
详细信息
Projection Pursuit is a classic exploratory technique for finding interesting projections of a dataset. We propose a method for recovering projections containing either Imbalanced Clusters or a Bernoulli-Rademacher di...
详细信息
We present a novel universal gradient method for solving convex optimization problems. Our algorithm—Dual Averaging with Distance Adaptation (DADA)—is based on the classical scheme of dual averaging and dynamically ...
详细信息
Multi-objective multi-armed bandit (MO-MAB) problems traditionally aim to achieve Pareto optimality. However, real-world scenarios often involve users with varying preferences across objectives, resulting in a Pareto-...
详细信息
The main challenge of nonconvex optimization is to find a global optimum, or at least to avoid "bad" local minima and meaningless stationary points. We study here the extent to which algorithms, as opposed t...
详细信息
暂无评论