A team of researchers from Pennsylvania State University, USA, and King Abdullah University of Science and Technology, Saudi Arabia, have proposed a novel method for resolving nonlinear partial differential equations (PDEs) with multiple solutions. Their method, called the Newton Informed Neural Operator (NINO), utilises neural network techniques and is based on operator learning. This approach…
Community Question Answering (CQA) platforms like Quora, Yahoo! Answers, and StackOverflow are popular online forums for information exchange. However, due to the variable quality of responses, users often struggle to sift through myriad answers to find pertinent information. Traditional methods of answer selection in these platforms include content/user modeling and adaptive support. Still, there's room…
Research conducted by DeepMind has shed new light on the complexities of machine learning and neural representation, providing insights into the dissociations between representation and computation in deep networks. High capacity deep networks frequently demonstrate an implicit bias towards simplicity amidst their learning dynamics and structure. The employed simple functions allow for easier learning of…
Federated learning is a way to train models collaboratively using data from multiple clients, maintaining data privacy. Yet, this privacy can become compromised by gradient inversion attacks that reconstruct original data from shared gradients. To address this threat and specifically tackle the challenge of text recovery, researchers from INSAIT, Sofia University, ETH Zurich, and LogicStar.ai…
Causal models play a vital role in establishing the cause-and-effect associations between variables in complex systems, though they struggle to estimate probabilities associated with multiple interventions and conditions. Two main types of causal models have been the focus of AI research - functional causal models and causal Bayesian networks (CBN).
Functional causal models make it…
Large language models (LLMs) have rapidly improved over time, proving their prowess in text generation, summarization, translation, and question-answering tasks. These advancements have led researchers to explore their potential in reasoning and planning tasks.
Despite this growth, evaluating the effectiveness of LLMs in these complex tasks remains a challenge. It's difficult to assess if any performance…
Large Language Models (LLMs) have revolutionized natural language processing tasks, and their potential in physical world planning tasks is beginning to be leveraged. However, these models often encounter problems in understanding the actual world, resulting in hallucinatory actions and a reliance on trial-and-error behavior. Researchers have noted that humans perform tasks efficiently by leveraging global…
Unleashing the Capabilities of SirLLM: Progress in Enhancing Memory Retention and Attention Systems.
The rapid advancement of large language models (LLMs) has paved the way for the development of numerous Natural Language Processing (NLP) applications, including chatbots, writing assistants, and programming tools. However, these applications often necessitate infinite input lengths and robust memory capabilities, features currently lacking in existing LLMs. Preserving memory and accommodating infinite input lengths remain…
Machine Translation (MT), part of Natural Language Processing (NLP), aims to automate the translation of text from one language to another using large language models (LLMs). The goal is to improve translation accuracy for better global communication and information exchange. The challenge in improving MT is using high-quality, diverse training data for instruction fine-tuning, which…