Before the development of PILOT (PIecewise Linear Organic Tree), linear model trees were slow to fit and susceptible to overfitting, notably with large datasets. The traditional regression trees faced challenges capturing linear relationships efficiently. Linear model trees also encountered problems with interpretability when integrating linear models in leaf nodes. The research points out the need…
Multi-target multi-camera tracking (MTMCT) has become indispensable in intelligent transportation systems, yet real-world applications are complex due to a shortage of publicly available data and laborious manual annotation. MTMCT involves tracking vehicles across multiple camera lenses, detecting objects, carrying out multi-object tracking, and finally clustering trajectories to generate a comprehensive image of vehicle movement. MTMCT…
In the domain of visual question answering (VQA), the Multi-Image Visual Question Answering (MIQA) remains a major hurdle. It entails generating pertinent and grounded responses to natural language prompts founded on a vast assortment of images. While large multimodal models (LMMs) have proven competent in single-image VQA, they falter when dealing with queries involving an…
Large Language Models (LLMs) have shown vast potential in various critical sectors, such as finance, healthcare, and self-driving cars. Typically, these LLM agents use external tools and databases to carry out tasks. However, this reliance on external sources has raised concerns about their trustworthiness and vulnerability to attacks. Current methods of attack against LLMs often…
General circulation models (GCMs) are crucial in weather and climate prediction. They work using numerical solvers for big scale dynamics and parameterizations for smaller processes like cloud formation. Despite continuous enhancements, difficulties still persist, including errors, biases, and uncertainties in long-term weather projections and severe weather events. Recently introduced machine-learning models have shown excellent results…
The significant progress in Artificial Intelligence (AI) and Machine Learning (ML) has underscored the crucial need for extensive, varied, and high-quality datasets to train and test basic models. Gathering such datasets is a challenging task due to issues like data scarcity, privacy considerations, and expensive data collection and annotation. Synthetic or artificial data has emerged…
Researchers from the University of California, Berkeley, have recently shed light on developing the performance of large language models (LLMs) in the field of Natural Language Processing (NLP). In spite of showing a high degree of language comprehension, LLMs display limitations in reliable and flexible reasoning. This can be attributed to the structural operation of…