Graph Neural Networks (GNNs) have found applications in various domains, such as natural language processing, social network analysis, recommendation systems, etc. Due to its widespread usage, improving the defences of GNNs has emerged as a critical challenge. While exploring the mechanisms vulnerable to an attack, researchers came across Bit Flip Attacks (BFAs). Conventionally, BFAs were… →

Despite progress in AI-driven human animation, existing models often face limitations in motion realism, adaptability, and scalability. Many models struggle to generate fluid body movements and rely on filtered training datasets, restricting their ability to handle varied scenarios. Facial animation has seen improvements, but full-body animations remain challenging due to inconsistencies in gesture accuracy and… →

In this tutorial, we’ll walk through how to set up and perform fine-tuning on the Llama 3.2 3B Instruct model using a specially curated Python code dataset. By the end of this guide, you’ll have a better understanding of how to customize large language models for code-related tasks and practical insight into the tools and… →

Vision-language models (VLMs) face a critical challenge in achieving robust generalization beyond their training data while maintaining computational resources and cost efficiency. Approaches, such as chain-of-thought supervised fine-tuning (CoT-SFT), often lead to overfitting, where models perform well on seen data but struggle with new, unseen scenarios. This limitation reduces their effectiveness in applications that demand… →

Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement learning to align models with human preferences and specific task requirements. Synthetic data is crucial, allowing researchers to evaluate and optimize post-training techniques. However, open research in this domain is… →

The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations due to their fixed context windows, which can lead to loss of relevant information over time. While Retrieval-Augmented Generation (RAG) methods provide external knowledge to supplement LLMs, they often rely on static document… →