In recent years, speech synthesis has undergone a profound transformation thanks to the emergence of large-scale generative models. This evolution has led to significant strides in zero-shot speech synthesis systems, including text-to-speech (TTS), voice conversion (VC), and editing. These systems aim to generate speech by incorporating unseen speaker characteristics from a reference audio segment during… →

The interdisciplinary domain of vision-language representation seeks innovative methods to develop systems to understand the nuanced interactions between text and images. This area is pivotal as it enables machines to process and interpret the vast amount of digitally available visual and textual content. Despite significant advances, the challenge persists primarily due to the noisy data… →

Neuromorphic computing represents a transformative approach to artificial intelligence, seeking to emulate the human brain’s neural structures and processing methods. This computing paradigm offers significant advancements in efficiency and performance for specific tasks, including those requiring real-time processing and low power consumption. Here, we explore the algorithms that drive neuromorphic computing, its potential use cases,… →

In artificial intelligence, a significant focus has been on developing models that simultaneously process and interpret multiple forms of data. These multimodal models are designed to analyze and synthesize information from various sources, such as text, images, and audio, mimicking human sensory and cognitive processes. The main challenge in this field is developing systems that… →

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess textual features providing context. Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. With… →