Graph Transformers (GTs) have successfully achieved state-of-the-art performance on various platforms. GTs can capture long-range information from nodes that are at large distances, unlike the local message-passing in graph neural networks (GNNs). In addition, the self-attention mechanism in GTs permits each node to look at other nodes in a graph directly, helping collect information from…
Artificial intelligence is constantly advancing, and there’s always something new to be excited about. A few moments ago, a cutting-edge AI model called “gpt2-chatbot” was making waves in X’s AI community (Twitter). This new large language model (LLM) has generated a lot of discussion and curiosity among AI experts and enthusiasts, who are eager to…
With the significant development in the rapidly developing field of Artificial Intelligence driven healthcare, a team of researchers has introduced OpenBioLLM-Llama3-70B & 8B models. These state-of-the-art Large Language Models (LLMs) have the potential to completely transform medical natural language processing (NLP) by establishing new standards for functionality and performance in the biomedical field. The release…
Instant Voice Cloning (IVC) in Text-to-Speech (TTS) synthesis, also known as Zero-shot TTS, allows TTS models to replicate the voice of any given speaker with just a short audio sample without requiring additional training on that speaker. While existing methods like VALLE and XTTS can replicate tone color, they need more flexibility in controlling style…
Physics-Informed Neural Networks (PINNs) have become a cornerstone in integrating deep learning with physical laws to solve complex differential equations, marking a significant advance in scientific computing and applied mathematics. These networks offer a novel methodology for encoding differential equations directly into the architecture of neural networks, ensuring that solutions adhere to the fundamental laws…
The success of many reinforcement learning (RL) techniques relies on dense reward functions, but designing them can be difficult due to expertise requirements and trial and error. Sparse rewards, like binary task completion signals, are easier to obtain but pose challenges for RL algorithms, such as exploration. Consequently, the question emerges: Can dense reward functions…
Evaluating Multimodal Large Language Models (MLLMs) in text-rich scenarios is crucial, given their increasing versatility. However, current benchmarks mainly assess general visual comprehension, overlooking the nuanced challenges of text-rich content. MLLMs like GPT-4V, Gemini-Pro-Vision, and Claude-3-Opus showcase impressive capabilities but lack comprehensive evaluation in text-rich contexts. Understanding text within images requires interpreting textual and visual…
Computer vision, machine learning, and data analysis across many fields have all seen a surge in the usage of synthetic data in the past few years. Synthetic means to mimic complicated situations that would be challenging, if not impossible, to record in the actual world. Information about individuals, such as patients, citizens, or customers, along…
Language models based on the transformers are pivotal in advancing the field of AI. Traditionally, these models have been deployed to interpret and generate human language by predicting token sequences, a fundamental process in their operational framework. Given their broad application, from automated chatbots to complex decision-making systems, improving their efficiency and accuracy remains a…
In computational linguistics, much research focuses on how language models handle and interpret extensive textual data. These models are crucial for tasks that require identifying and extracting specific information from large volumes of text, presenting a considerable challenge in ensuring accuracy and efficiency. A critical challenge in processing extensive text data is the model’s ability…