Machine learning has revolutionized various fields, offering powerful tools for data analysis and predictive modeling. Central to these models’ success is hyperparameter optimization (HPO), where the parameters that govern the learning process are tuned to achieve the best possible performance. HPO involves selecting hyperparameter values such as learning rates, regularization coefficients, and network architectures. These…
Hypergraphs, which extend traditional graphs by allowing hyperedges to connect multiple nodes, offer a richer representation of complex relationships in fields like social networks, bioinformatics, and recommender systems. Despite their versatility, generating realistic hypergraphs is challenging due to their complexity and the need for effective generative models. While traditional methods focus on algorithmic generation with…
Spiking Neural Networks (SNNs) hold significant promise in developing energy-efficient and biologically plausible artificial neural networks. However, a critical challenge is their limited ability to handle sequential tasks such as text classification and time-series forecasting. This limitation primarily stems from the lack of an effective spike-form positional encoding (PE) mechanism, which is crucial for capturing…
Information management and retrieval systems are essential for businesses and organizations, whether for customer support, internal knowledge bases, academic research, or instructional purposes. It can be challenging to manage enormous data volumes while ensuring users can quickly locate what they need. Regarding privacy issues, language support, and ease of use, existing tools frequently need to…
Large Language Models (LLMs) have become increasingly important in cybersecurity, particularly in their application to secure coding practices. As these AI-driven models can generate human-like text, they are now being utilized to detect and mitigate security vulnerabilities in software. The primary goal is to harness these models to enhance the security of code, which is…
Accurately transcribing spoken language into written text is becoming increasingly essential in speech recognition. This technology is crucial for accessibility services, language processing, and clinical assessments. However, the challenge lies in capturing the words and the intricate details of human speech, including pauses, filler words, and other disfluencies. These nuances provide valuable insights into cognitive…
Artificial intelligence is rapidly advancing, with a significant focus on improving models that process and interpret complex datasets, particularly time series data. This type of data involves sequences of data points collected over time and is critical in various fields, including finance, healthcare, and environmental science. The ability to accurately predict and classify time series…
Label-efficient segmentation has emerged as a crucial area of research, particularly in point cloud semantic segmentation. While deep learning techniques have advanced this field, the reliance on large-scale datasets with point-wise annotations remains a significant challenge. Recent methods have explored weak supervision, human annotations, and techniques such as perturbed self-distillation, consistency regularization, and self-supervised learning…
Understanding social interactions in complex real-world settings requires deep mental reasoning to infer the underlying mental states driving these interactions, known as the Theory of Mind (ToM). Social interactions are often multi-modal, involving actions, conversations, and past behaviors. For AI to effectively engage in human environments, it must grasp these mental states and their interrelations.…
Artificial intelligence (AI) has increasingly relied on vast and diverse datasets to train models. However, a major issue has arisen regarding these datasets’ transparency and legal compliance. Researchers and developers often use large-scale data without fully understanding its origins, proper attribution, or licensing terms. As AI continues to expand, these data transparency and licensing gaps…