Researchers from Imperial College London and Dell have developed a new framework for transferring styles to images using text prompts to guide the process while maintaining the substance of the original image. This advanced model, called StyleMamba, addresses the computational requirements and training inefficiencies present in current text-guided stylization techniques.
Traditionally, text-driven stylization requires significant computational…
Multimodal large language models (MLLMs) represent an advanced fusion of computer vision and language processing. These models have evolved from predecessors, which could only handle either text or images, to now being capable of tasks that require integrated handling of both. Despite these evolution, a highly complex issue known as 'hallucination' impairs their abilities. 'Hallucination'…
Generative AI (GenAI) tools have developed significantly since their inception in the 1960s when they were first introduced in a Chatbot. However, they only truly began to gain popularity in 2014 with the introduction of generative adversarial networks (GANs), a type of machine learning technology that enabled GenAI to authentically design realistic images, audio, and…
Language modeling, a key aspect of machine learning, aims to predict the likelihood of a sequence of words. Used in applications such as text summarization, translation, and auto-completion systems, it greatly improves the ability of machines to understand and generate human language. However, processing and storing large data sequences can present significant computational and memory…
Machine learning, with its wide application in finance for tasks such as credit scoring, fraud detection, and trading, has become an instrumental tool in analyzing big financial data. The technology is used to spot trends, predict outcomes, and automate decisions to enhance efficiency and profits. For those in the finance industry keen on pursuing these…
Graph Neural Networks (GNNs) are essential for processing complex data structures in domains such as e-commerce and social networks. However, as graph data volume increases, existing systems struggle to efficiently handle data that exceed memory capacity. This warrants out-of-core solutions where data resides on disk. Yet, such systems have faced challenges balancing speed of data…
The artificial intelligence (AI) landscape continues to evolve, with OpenAI launching its latest Language Learning Model (LLM), GPT-4. This new version not only enhances creativity, accuracy, and safety but also incorporates multimodal functionalities, processing images, PDFs, and CSVs. The introduction of the Code Interpreter means GPT-4 can now execute its own code to improve accuracy…
Language models (LMs) are becoming increasingly important in the field of software engineering. They serve as a bridge between users and computers, improving code generated by LMs based on feedback from the machines. LMs have made significant strides in functioning independently in computer environments, which could potentially fast-track the software development process. However, the practical…
Large language models (LLMs) have introduced ground-breaking advancements to the field of natural language processing, such as improved machine translation, question-answering, and text generation. Yet, training these complex models poses significant challenges, including high resource requirements and lengthy training times.
Former methods addressing these concerns involved loss-scaling and mixed-precision strategies, which aimed to further training efficiency…
The process of data cleaning is a crucial step in Natural Language Processing (NLP) tasks, particularly before tokenization and when dealing with text data that contains unusual word separations like underscores, slashes, or other symbols in place of spaces. The reason for its importance is that tokenizers often depend on spaces to split text into…