Tootfinder

Buena Vista Social Club gibt's grad in der Mediathek.
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(1/3) Please join me today on Zoom at 12pm ET / 11am CT / 10am MT / 9am PT for "Beyond the Algorithmic Feed," a free MediaEd Club webinar for the Media Education Lab:
https://mediaeducationlab.com/index.php/events/beyond-algorithmic-feed
While som…

An analysis of the top 100 trending TikTok videos under #mentalhealthtips finds 52 contain misinformation, including misused language and quick-fix methods (The Guardian)

Optimized methods for composite optimization: a reduction perspective
Jinho Bok, Jason M. Altschuler
https://arxiv.org/abs/2506.23756 https://

Optimized methods for composite optimization: a reduction perspective
Recent advances in convex optimization have leveraged computer-assisted proofs to develop optimized first-order methods that improve over classical algorithms. However, each optimized method is specially tailored for a particular problem setting, and it is a well-documented challenge to extend optimized methods to other settings due to their highly bespoke design and analysis. We provide a general framework that derives optimized methods for composite optimization directly from those for uncons…

A Class of Stochastic Runge-Kutta Methods for Stochastic Differential Equations Converging with Order 1 in $L^p$-Norm
Andreas R\"o{\ss}ler
https://arxiv.org/abs/2506.22657

A Class of Stochastic Runge-Kutta Methods for Stochastic Differential Equations Converging with Order 1 in $L^p$-Norm
For the approximation of solutions for Itô and Stratonovich stochastic differential equations (SDEs)a new class of efficient stochastic Runge-Kutta (SRK) methods is developed. As the main novelty only two stages are necessary for the proposed SRK methods of order 1 that can be applied to SDEs with non-commutative or with commutative noise. In addition, a variant of the SRK method for SDEs with additive noise is presented. All proposed SRK methods cover also the case of drift-implicit schemes a…

Impact of Fine-Tuning Methods on Memorization in Large Language Models
Jie Hou, Chuxiong Wu, Lannan Luo, Qiang Zeng
https://arxiv.org/abs/2507.00258 https:…

Impact of Fine-Tuning Methods on Memorization in Large Language Models
As the capabilities of pre-trained large language models (LLMs) continue to advance, the "pre-train and fine-tune" paradigm has become increasingly mainstream, leading to the development of various fine-tuning methods. However, the privacy risks arising from memorization during fine-tuning have received relatively little attention. To address this gap, we categorize popular fine-tuning approaches and assess their impact on memorization through the lens of membership inference attacks (MIAs). Ou…

An analysis of the top 100 trending TikTok videos under #mentalhealthtips finds 52 contain misinformation, including misused language and quick-fix methods (The Guardian)

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
Bosubabu Sambana, Kotamsetty Geethika Devi, Bandi Rajeswara Reddy, Galeti Mohammad Hussain, Gownivalla Siddartha
https://arxiv.org/abs/2506.23504

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and pr…

Please join me on Monday (noon Eastern time) for our August MediaEd Club webinar, "Searching with AI: Can We Trust the New Gatekeepers?" It's FREE on Zoom! Check out the recommended links first:
https://mediaeducationlab.com/events/searching-ai

AutoLyap: A Python package for computer-assisted Lyapunov analyses for first-order methods
Manu Upadhyaya, Adrien B. Taylor, Sebastian Banert, Pontus Giselsson
https://arxiv.org/abs/2506.24076

AutoLyap: A Python package for computer-assisted Lyapunov analyses for first-order methods
We introduce AutoLyap, a Python package designed to automate Lyapunov analyses for a wide class of first-order methods for solving structured optimization and inclusion problems. Lyapunov analyses are structured proof patterns, with historical roots in the study of dynamical systems, commonly used to establish convergence results for first-order methods. Building on previous works, the core idea behind AutoLyap is to recast the verification of the existence of a Lyapunov analysis as a semidefin…