Artificial Intelligence (AI) has made significant strides in the past few years with the advancements in Deep Learning (DL) and the advent of Large Language Models (LLMs). Many powerful applications have been developed that are capable of processing enormous amounts of data. Although these innovations speed up and optimize many aspects of our work, they…
Diffusion models are at the forefront of generative model research. These models, essential in replicating complex data distributions, have shown remarkable success in various applications, notably in generating intricate and realistic images. They establish a stochastic process that progressively adds noise to data, followed by a learned reversal of this process to create new data…
The landscape of language models is evolving rapidly, driven by the empirical success of scaling models with increased parameters and computational budgets. In this era of large language models, Mixture-of-Experts (MoE) architecture emerges as a key player, offering a solution to manage computational costs while scaling model parameters. However, challenges persist in ensuring expert specialization…
Molecular property prediction stands at the forefront of drug discovery and design, which has grown increasingly dependent on advancements in artificial intelligence and machine learning. Traditional methods, while foundational, often need to catch up in their scope, unable to encapsulate the vast and intricate details of molecular characteristics. This gap in capability highlights the need…
The Billion-Scale Approximate Nearest Neighbor Search Challenge, part of the NeurIPS competition track, aims to advance research in large-scale ANNS (Approximate Nearest Neighbor Search). BigANN is a collaborative arena where the best minds in the field come together to push the boundaries of vector search technology. Participants face four distinct tracks, each tackling a different…
MLLMs, or multimodal large language models, have been advancing lately. By incorporating images into large language models (LLMs) and harnessing the capabilities of LLMs, MLLMs demonstrate exceptional skill in tasks including visual question answering, instruction following, and image understanding. Studies have seen a significant flaw in these models despite their improvements; they still have some…
The dependence on external servers for AI applications can pose risks, including data security and reliance on a stable internet connection. While some alternatives exist, most of them still require an internet connection for functioning. This leaves users searching for a solution that combines the power of AI with the comfort of offline usage. Currently,…
The field of research focuses on integrating machine learning (ML) in healthcare for personalized treatment. This innovative approach aims to revolutionize how we understand and apply medical treatments, shifting from one-size-fits-all solutions derived from traditional clinical trials to more nuanced, individualized care. The essence of this research lies in predicting treatment outcomes tailored to individual…
In user-centric applications like personal assistance and customer support, language models are increasingly being deployed as dialogue agents in the rapidly advancing domain of artificial intelligence. These agents are tasked with understanding and responding to various user queries and tasks, a capability that hinges on their ability to adapt to new scenarios quickly. However, customizing…
While generating realistic tabular data, one of the difficulties faced by the researchers is maintaining privacy, especially in sensitive domains like finance and healthcare. As the amount of data and the importance of data analysis is increasing in all fields and privacy concerns are leading to hesitancy in deploying AI models, the importance of maintaining…
