Tootfinder

Email: Ron Conway, a close friend of Marc Benioff for 25 years, stepped down from the Salesforce Foundation board, citing Benioff's comments supporting Trump (Heather Knight/New York Times)
https://www.nytimes.com/2025/10…

Venture Capitalist Ron Conway Resigns From Board Over Benioff National Guard Comments
Ron Conway stepped down from the board of Salesforce’s philanthropic arm after the company’s chief executive, Marc Benioff, said he supported President Trump and wanted the National Guard to come to San Francisco.

J6 Committee Chair: Murder of Nat'l Guard Is An 'Unfortunate Accident' (Ed Morrissey/HotAir)
https://hotair.com/ed-morrissey/2025/12/11/dem-statistics-lies-damned-lies-and-unfortunate-accidents-n3809772
http://www.memeorandum.com/251211/p114#a251211p114

Marc Benioff says President Trump is "doing a great job" and believes National Guard troops should be deployed to San Francisco to help reduce crime in the city (Heather Knight/New York Times)
https://www.nytimes.com/202…

Salesforce CEO Marc Benioff Says Trump Should Send National Guard to San Francisco
Mr. Benioff, the Salesforce C.E.O. and owner of Time magazine, once supported Hillary Clinton and a business tax for homeless services. Now he’s fully behind Donald Trump.

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter
https://arxiv.org/abs/2510.12363 https://

Pretraining in Actor-Critic Reinforcement Learning for Robot Motion Control
The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcement learning (RL) for robot motion control, individual skills are often learned from scratch despite the high likelihood that some generalizable knowledge is shared across all task-specific policies belonging to a single robot embodiment. This work aims to define a paradigm for pretraining neural network models that enc…

Marc Benioff Says Trump Should Send Guard Troops to San Francisco (Heather Knight/New York Times)
https://www.nytimes.com/2025/10/10/us/marc-benioff-san-francisco-guard.html
http://www.memeorandum.com/251010/p154#a251010p154

On goodness-of-fit testing for volatility in McKean-Vlasov models
Akram Heidari, Mark Podolskij
https://arxiv.org/abs/2510.12607 https://arxiv.org/pdf/2510…

On goodness-of-fit testing for volatility in McKean-Vlasov models
This paper develops a statistical framework for goodness-of-fit testing of volatility functions in McKean-Vlasov stochastic differential equations, which describe large systems of interacting particles with distribution-dependent dynamics. While integrated volatility estimation in classical SDEs is now well established, formal model validation and goodness-of-fit testing for McKean-Vlasov systems remain largely unexplored, particularly in regimes with both large particle limits and high-frequen…

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
https://arxiv.org/abs/2510.06982 https://…

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that intermittently replaces finetuned weights with their pretrained reference, through the lens of a single-run, weight-sharing implicit ensemble. This perspective reveals three key levers that govern robustness: the \emph{masking anchor}, \emph{resampling frequency}, and \emph{mask sparsity}. Guided by this analysis, we…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Revisiting Mixout: An Overlooked Path to Robust Finetuning
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Lennart Werner, Pol Eyschen, Sean Costello, Pierluigi Micarelli, Marco Hutter
https://arxiv.org/abs/2510.11574

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Accurate real-time estimation of end effector interaction forces in hydraulic excavators is a key enabler for advanced automation in heavy machinery. Accurate knowledge of these forces allows improved, precise grading and digging maneuvers. To address these challenges, we introduce a high-accuracy, retrofittable 2D force- and payload estimation algorithm that does not impose additional requirements on the operator regarding trajectory, acceleration or the use of the slew joint. The approach is …

High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
https://arxiv.org/abs/2510.06955 …

High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial computational costs due to the need to train and store multiple models. Dropout offers a lightweight alternative by simulating ensembles through random neuron deactivation; however, when applied to pre-trained models, it tends to over-regularize and disrupt critical representations necessary for generalization. In this wor…