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Predictive analytics is pivotal in driving data driven decision making within product development processes. By harnessing advanced statistical algorithms and machine learning techniques, predictive analytics empowers organizations to make informed c...

Hey everyone, I 39 m excited to announce the release of my first ever Golang package GoES Go Evolution Strategy for Black Box Optimization GoES is library that implements Adaptive Evolution Strategies ES , powerful optimization technique for prob...

Find better prompts by exploring large set of prompt variants automaticallyPhoto by Icons8 Team onUnsplashIts no secret that much of the success of LLMs still depends on our ability to prompt them with the right instructions and examples. As newer ge...

New tokenizer, optimized pretraining and some other details about Meta AIs newmodel.Created UsingIdeogramI recently started an AI focused educational newsletter, that already has over 165,000 subscribers. TheSequence is no BS meaning no hype, no new...

Ever pondered about the game changing impact of generative AI in healthcare It 8217 s truly remarkable how this advanced technology is transforming diagnostics, treatment personalization, and medical research, leading to better outcomes for patient...

We study a general scalarization approach via utility functions in multi-objective optimization. It consists of maximizing utility which is obtained from the objectives' bargaining with regard to a disagreement reference point. The theoretical framework for a broad class of utility functions from microeconomics is developed. For that, we associate a utility-dependent single-objective optimization problem with the given multi-objective optimization problem. We show that Pareto optimal points of t...

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances. To this end, we select smal...

Hello everyone, after period of testing and iteration, our version 3.8.3 has entered the testing stage. In addition to focusing on fixing issues reported in version 3.8, version 3.8.3 also includes some functionality adjustments. We now invite commun...

Organizations are facing ever increasing requirements for sustainability goals alongside environmental, social, and governance ESG practices. Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organizations i...

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization arXiv Continue reading on Medium...

Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve, falling into the category of NP-hard problems. In such scenarios, classical algorithms,...

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Dissipative Gradient Descent Ascent Method Control Theory Inspired Algorithm for Min max Optimization arXiv Continue reading on Medium...

Created Using IdeogramNext Week in The Sequence Edge 389 In our series about autonomous agents, we discuss the concept of large action models LAMs . We review the LAM research pioneered by the team from Rabbit and we dive into the MetaGPT framework ...

In this Help Net Security interview, Roy Davis, Manager 8211 Vulnerability Management 038 Bug Bounty at Zoom, discusses the role bug bounty programs play in identifying security vulnerabilities and facilitating collaboration with researchers. H...

This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type structure with dense layers added at the bottleneck region to capture complex geometrical features. The network is trained using datasets obtained by the problem-specific open-source TO codes. Tensorflow and Keras are the main libraries employed to develop and to t...

In digital marketing, the strategic management of brand ad spend is crucial for achieving maximum impact and efficiency. Balancing the immediate visibility provided by Pay Per Click PPC advertising with the sustainable growth driven by SEO is key. ...

In the smart grid, the prosumers can sell unused electricity back to the power grid, assuming the prosumers own renewable energy sources and storage units. The maximizing of their profits under a dynamic electricity market is a problem that requires intelligent planning. To address this, we propose a framework based on Proximal Policy Optimization (PPO) using recurrent rewards. By using the information about the rewards modeled effectively with PPO to maximize our objective, we were able to get ...

This AI prompt formula should ease your AI prompting woes....