Generic transport equations, which consist of time-dependent partial differential equations (PDEs), model the movement of extensive properties like mass, momentum, and energy in physical systems. Originating from conservation laws, such equations shed light on a range of physical phenomena, extending from mass diffusion to Navier-Stokes equations. In science and engineering fields, these PDEs can be…
Natural Language Processing (NLP) is an evolving field in which large language models (LLMs) are becoming increasingly important. The fine-tuning of these models has emerged as a critical process for enhancing their specific functionalities without imposing substantial computational demands. In this regard, researchers have been focusing on LLM modifications to ensure optimal performance even with…
Significant advancements have been made in the field of robotics research with the integration of large language models (LLMs) into robotic systems. This development has enabled robots to better tackle complex tasks that demand detailed planning and sophisticated manipulation, bridging the gap between high-level planning and robotic control. However, challenges persist in transforming the remarkable…
Researchers in the field of large language models (LLMs) are focused on training these models to respond more effectively to human-generated text. This requires aligning the models with human preferences, reducing bias, and ensuring the generation of useful and safe responses, a task often achieved through supervised fine-tuning and complex pipelines like reinforcement learning from…
Neural Architecture Search (NAS) is a method used by researchers to automate the development of optimal neural network architectures. These architectures are created for a specific task and are then evaluated against a performance metric on a validation dataset. However, earlier NAS methods encountered several issues due to the need to extensively train each candidate…
Deep learning is continuously evolving with attention mechanism playing an integral role in improving sequence modeling tasks. However, this method significantly bogs down computation with its quadratic complexity, especially in hefty long-context tasks such as genomics and natural language processing. Despite efforts to enhance its computational efficiency, existing techniques like Reformer, Routing Transformer, and Linformer…
Artificial intelligence (AI) is becoming more sophisticated, requiring models capable of processing large-scale data and providing precise, valuable insights. The aim of researchers in this field is to develop systems that are capable of continuous learning and adaptation, ensuring relevance in dynamic environments.
One of the main challenges in developing AI models is the issue of…
Natural Language Processing (NLP) involves computers understanding and interacting with human language through language models (LMs). These models generate responses across various tasks, making the quality assessment of responses challenging. However, as proprietary models like GPT-4 increase in sophistication, they often lack transparency, control, and affordability, thus prompting the need for reliable open-source alternatives.
Existing…