Machine learning has seen significant advancements in integrating Bayesian approaches and active learning methods. Two notable research papers contribute to this development: “Bayesian vs. PAC-Bayesian Deep Neural Network Ensembles” by University of Copenhagen researchers and “Deep Bayesian Active Learning for Preference Modeling in Large Language Models” by University of Oxford researchers. Let’s synthesize the findings… →
CONCLUSIONS AND RELEVANCE: In this study, MM was more effective in decreasing addiction severity and gaming cravings compared with PMR. These findings indicate that MM may be an effective treatment for IGD and may exert its effects by altering frontopallidal pathways. →
CONCLUSION: While PSAPs provide some benefit for moderate to moderately severe unilateral hearing loss, HAs are more effective. This underscores the potential role of PSAPs as an accessible, affordable first-line intervention in hearing rehabilitation, particularly for individuals facing challenges in accessing conventional HAs. →
Nvidia recently announced the release of two groundbreaking technologies in artificial intelligence: HelpSteer2 and Llama3-70B-SteerLM-RM. These innovations promise to significantly enhance the capabilities of AI systems in various applications, from autonomous driving to natural language processing. Image Source [Dated 18th June 2024] HelpSteer2: Revolutionizing Autonomous Driving HelpSteer2 is Nvidia’s latest offering in autonomous driving. This… →
Large language models (LLMs) have made significant strides in handling multiple modalities and tasks, but they still need to improve their ability to process diverse inputs and perform a wide range of tasks effectively. The primary challenge lies in developing a single neural network capable of handling a broad spectrum of tasks and modalities while… →
In supervised multi-modal learning, data is mapped from various modalities to a target label using information about the boundaries between the modalities. Different fields have been interested in this issue: autonomous vehicles, healthcare, robots, and many more. Although multi-modal learning is a fundamental paradigm in machine learning, its efficacy differs depending on the task at… →
Data-driven methods that convert offline datasets of prior experiences into policies are a key way to solve control problems in various fields. There are mainly two approaches for learning policies from offline data, imitation learning and offline reinforcement learning (RL). Imitation learning needs high-quality demonstration data, while offline reinforcement learning RL can learn effective policies… →