In the rapidly advancing field of natural language processing (NLP), the advent of large language models (LLMs) has significantly transformed. These models have shown remarkable success in understanding and generating human-like text across various tasks without specific training. However, the deployment of such models in real-world scenarios is often hindered by their substantial demand for…
The field of Artificial Intelligence (AI) has always had a long-standing goal of automating everyday computer operations using autonomous agents. Basically, the web-based autonomous agents with the ability to reason, plan, and act are a potential way to automate a variety of computer operations. However, the main obstacle to accomplishing this goal is creating agents…
In the ever-evolving landscape of natural language processing (NLP), the quest to bridge the gap between machine interpretation and the nuanced complexity of human language continues to present formidable challenges. Central to this endeavor is the development of large language models (LLMs) capable of parsing and fully understanding the contextual nuances underpinning human communication. This…
Optical flow estimation, a cornerstone of computer vision, enables predicting per-pixel motion between consecutive images. This technology fuels advancements in numerous applications, from enhancing action recognition and video interpolation to improving autonomous navigation and object tracking systems. Traditionally, progress in this domain has been propelled by developing more complex models that promise higher accuracy. However,…
With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). These models incorporate physical laws into the learning process, promising a significant leap in predictive accuracy and robustness. But as PINNs grow in depth and…
Retrieval-augmented language models often retrieve only short chunks from a corpus, limiting overall document context. This decreases their ability to adapt to changes in the world state and incorporate long-tail knowledge. Existing retrieval-augmented approaches also need fixing. The one we tackle is that most existing methods retrieve only a few short, contiguous text chunks, which…
Large Language Models (LLMs) are a recent trend as these models have gained significant importance for handling tasks related to Natural Language Processing (NLP), such as question-answering, text summarization, few-shot learning, etc. But the most powerful language models are released by keeping the important aspects of the model development under wraps. This lack of openness…
Speech recognition technology has become a cornerstone for various applications, enabling machines to understand and process human speech. The field continuously seeks advancements in algorithms and models to improve accuracy and efficiency in recognizing speech across multiple languages and contexts. The main challenge in speech recognition is developing models that accurately transcribe speech from various…
Language models stand as titans, harnessing the vast expanse of human language to power many applications. These models have revolutionized how machines understand and generate text, enabling translation, content creation, and conversational AI breakthroughs. Their huge size is a source of their prowess and presents formidable challenges. The computational heft required to operate these behemoths…
In recent years, researchers in the field of robotic reinforcement learning (RL) have achieved significant progress, developing methods capable of handling complex image observations, training in real-world scenarios, and incorporating auxiliary data, such as demonstrations and prior experience. Despite these advancements, practitioners acknowledge the inherent difficulty in effectively utilizing robotic RL, emphasizing that the specific…
