Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.
🌀 Abstract: This paper tackles a critical issue in text-to-image diffusion models like Stable Diffusion, DALL·E, and Midjourney. These models are trained on massive datasets, often containing private or copyrighted content, which raises serious legal and ethical concerns. To address this, machine unlearning methods have emerged, aiming to remove specific information from the models. However, this paper reveals a major flaw: these unlearned concepts can come back when the model is fine-tuned. The authors introduce a new framework to analyze and evaluate the stability of current unlearning techniques and offer insights into why they often fail, paving the way for more robust future methods.
Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.
🌀 Abstract: This paper tackles a critical issue in text-to-image diffusion models like Stable Diffusion, DALL·E, and Midjourney. These models are trained on massive datasets, often containing private or copyrighted content, which raises serious legal and ethical concerns. To address this, machine unlearning methods have emerged, aiming to remove specific information from the models. However, this paper reveals a major flaw: these unlearned concepts can come back when the model is fine-tuned. The authors introduce a new framework to analyze and evaluate the stability of current unlearning techniques and offer insights into why they often fail, paving the way for more robust future methods.
In general, many financial experts support their clients’ desire to buy cryptocurrency, but they don’t recommend it unless clients express interest. “The biggest concern for us is if someone wants to invest in crypto and the investment they choose doesn’t do well, and then all of a sudden they can’t send their kids to college,” says Ian Harvey, a certified financial planner (CFP) in New York City. “Then it wasn’t worth the risk.” The speculative nature of cryptocurrency leads some planners to recommend it for clients’ “side” investments. “Some call it a Vegas account,” says Scott Hammel, a CFP in Dallas. “Let’s keep this away from our real long-term perspective, make sure it doesn’t become too large a portion of your portfolio.” In a very real sense, Bitcoin is like a single stock, and advisors wouldn’t recommend putting a sizable part of your portfolio into any one company. At most, planners suggest putting no more than 1% to 10% into Bitcoin if you’re passionate about it. “If it was one stock, you would never allocate any significant portion of your portfolio to it,” Hammel says.
Launched in 2013, Telegram allows users to broadcast messages to a following via “channels”, or create public and private groups that are simple for others to access. Users can also send and receive large data files, including text and zip files, directly via the app.The platform said it has more than 500m active users, and topped 1bn downloads in August, according to data from SensorTower.telegram from kr
