In recent years, text-to-speech (TTS) technology has made significant strides, yet numerous challenges still remain. Autoregressive (AR) systems, while offering diverse prosody, tend to suffer from robustness issues and slow inference speeds. Non-autoregressive (NAR) models, on the other hand, require explicit alignment between text and speech during training, which can lead to unnatural results. The…
Machine learning for predictive modeling aims to forecast outcomes based on input data accurately. One of the primary challenges in this field is “domain adaptation,” which addresses differences between training and application scenarios, especially when models face new, varied conditions after training. This challenge is significant for tabular finance, healthcare, and social sciences datasets, where…
Messenger RNA (mRNA) plays a crucial role in protein synthesis, translating genetic information into proteins via a process that involves sequences of nucleotides called codons. However, current language models used for biological sequences, especially mRNA, fail to capture the hierarchical structure of mRNA codons. This limitation leads to suboptimal performance when predicting properties or generating…
Theory of Mind (ToM) capabilities – the ability to attribute mental states and predict behaviors of others – have become increasingly critical as Large Language Models (LLMs) become more integrated into human interactions and decision-making processes. While humans naturally infer others’ knowledge, anticipate actions, and expect rational behaviors, replicating these sophisticated social reasoning abilities in…
MicroRNAs (miRNAs) play key roles in human diseases, including cancer and infectious diseases, by regulating gene expression. Modulating miRNAs or their gene targets with small molecules present a potential therapeutic approach for correcting disease-related cellular dysfunctions. However, predicting effective small molecules for specific miRNAs is difficult due to limited data on miRNA-small molecule interactions. Although…
In a data-driven world, privacy and security have become pressing concerns for individuals and organizations alike. With data breaches and information misuse becoming alarmingly frequent, safeguarding sensitive information is critical. Among the most challenging aspects of data protection is managing Personally Identifiable Information (PII), such as names, addresses, and social security numbers, which are highly…
Retrieval-augmented generation (RAG) systems, a key area of research in artificial intelligence, aim to enhance large language models (LLMs) by incorporating external sources of information for generating responses. This approach is particularly valuable in fields requiring accurate, fact-based answers, such as question-answering or information retrieval tasks. Yet, these systems often encounter substantial challenges in filtering…
The rise of AI-assisted coding has undoubtedly revolutionized software development, but not without its challenges. One of the main pain points for developers has been the lack of choice and flexibility in selecting AI models that best suit their unique needs. GitHub Copilot, which emerged as a groundbreaking tool for code generation and assistance, has…
Multimodal large language models (MLLMs) rapidly evolve in artificial intelligence, integrating vision and language processing to enhance comprehension and interaction across diverse data types. These models excel in tasks like image recognition and natural language understanding by combining visual and textual data processing into one coherent framework. This integrated approach allows MLLMs to perform highly…
Retrieval-Augmented Generation (RAG) is a framework that enhances language models by combining two main components: Retriever and Generator. A RAG pipeline combines the retriever and generator in an iterative process and is widely used in open-domain question-answer, knowledge-based chatbots, and specialized information retrieval tasks where the accuracy and relevance of real-world data are crucial. Despite…