Hi all. So I just learned about Monte-Carlo Tree Search from University of Queensland’s free book, and I have one question. From my understanding, each state has its own tree. Is it correct? If correct, then why? I thought that states that are closer to the root tree is already simulated, hence we can just reuse the calculation? Thank you in advance. submitted by /u/indo_dementor [link] [comments]
End-to-end AI tools that are tuned to specific industries and case types can dramatically boost productivity.
The medical journal The Lancet did not pull any punches in a scathing editorial on Robert F. Kennedy Jr., calling the anti-vaccine activist’s first year as US Health Secretary “a failure by most measures, especially his own.” The Lancet is one of the world’s oldest academic medical journals still in publication and one of the most cited sources of peer-reviewed medical research. But it is also well-known for publishing an infamous study by prominent anti-vaccine activist and disgraced […]
Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications. However, FL faces the dual challenge of ensuring learning quality and robust privacy protection while keeping resource consumption low, particularly when using computationally expensive techniques such as homomorphic encryption (HE). In this work, we enhance an FL process that preserves privacy using HE by integrating it with synthetic data generation and an interleaving strategy. Specifically, our […]
The real barrier to AI adoption is not training gaps, a lack of leadership clarity, or even fear—it’s invisibility.
Semi-supervised learning (SSL) often suffers under class imbalance, where pseudo-labeling amplifies majority bias and suppresses minority performance. We address this issue with a lightweight framework that, to our knowledge, is the first to introduce Proportion Loss from learning from label proportions (LLP) into SSL as a regularization term. Proportion Loss aligns model predictions with the global class distribution, mitigating bias across both majority and minority classes. To further stabilize training, we formulate a stochastic variant that accounts for […]
A conversation with HBS innovation expert Linda Hill on redefining your leadership role.
Graphical User Interface (GUI) Agents, benefiting from recent advances in multimodal large language models (MLLM), have achieved significant development. However, due to the frequent updates of GUI applications, adapting to new tasks without forgetting old tasks in GUI continual learning remains an open problem. In this work, we reveal that while Supervised Fine-Tuning (SFT) facilitates fast adaptation, it often triggers knowledge overwriting, whereas Reinforcement Learning (RL) demonstrates an inherent resilience that shields prior interaction logic from erasure. Based […]
I’ve been a fast typer my whole life. For decades, I optimized my input speed. I learned keyboard shortcuts, practiced touch typing, and prided myself on how quickly I could translate thought into text. It was the only bottleneck I knew. Then Large Language Models arrived, and suddenly I found myself waiting. Not for my own fingers, but for the machine to think. I’d type a prompt — something that took me thirty seconds to articulate — and then stare at a blinking cursor […]
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen domains or label spaces. While recent advancements have transitioned toward leveraging Large Language Models (LLMs) as predictors to enhance GNNs, these methods often suffer from cross-modal alignment issues. A recent paradigm (i.e., Graph-R1) overcomes the aforementioned architectural dependencies by adopting a purely text-based format and utilizing LLM-based graph reasoning, showing improved […]
