Browsing: ML News

When building machine learning (ML) models using preexisting datasets, experts in the field must first familiarize themselves with the data, decipher its structure, and determine which…

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression to align…

The recent advancements in machine learning, particularly in generative models, have been marked by the emergence of diffusion models (DMs) as powerful tools for modeling complex…

Despite the significant strides in large language models (LLMs) such as ChatGPT, Llama2, Vicuna, and Gemini, they grapple with safety issues. This paper introduces a novel…

Training large language models (LLMs) has posed a significant challenge due to their memory-intensive nature. The conventional approach of reducing memory consumption by compressing model weights…

In the vast expanse of machine learning applications, recommendation systems have become indispensable for tailoring user experiences in digital platforms, ranging from e-commerce to social media.…

In recent years, machine learning has significantly shifted away from the assumption that training and testing data come from the same distribution. Researchers have identified that…

Reinforcement Learning (RL) has become a cornerstone for enabling machines to tackle tasks that range from strategic gameplay to autonomous driving. Within this broad field, the…

Google DeepMind researchers have revealed a pioneering approach called AtP* to understand the behaviors of large language models (LLMs). This groundbreaking method stands on the shoulders…

Building and using appropriate benchmarks is a major driver of advancement in RL algorithms. For value-based deep RL algorithms, there’s the Arcade Learning Environment; for continuous…