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Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Nonetheless, despite their widespread use and accomplishments, ongoing research continues to delve into the intricate workings of these models, with a particular focus on the linear nature of intermediate embedding transformations. This less explored aspect poses significant implications for further advancements in…
Foundation models enable complex tasks such as natural language processing, image recognition, etc. These models leverage large datasets and intricate neural networks to deliver previously unattainable results with traditional algorithms. The use of these models has revolutionized the field of AI, allowing for more accurate and sophisticated analysis and interpretation of data. Researchers face the…
Regarding robotic learning, the standard practice is to use datasets tailored to the particular robot and job at hand to train policies. Starting from scratch in this manner necessitates a substantial amount of data collection for every activity, and the policies that are produced typically display little generalizability. Theoretically, data gathered from previous robots and…
Reinforcement learning (RL) is predicated on agents learning to make decisions by interacting with an environment. RL has achieved remarkable feats in various applications, including games, robotics, and autonomous systems. The goal is to develop algorithms that enable agents to perform tasks efficiently by maximizing cumulative rewards through trial-and-error interactions. By continuously adapting to new…
The proliferation of machine learning (ML) models in high-stakes societal applications has sparked concerns regarding fairness and transparency. Instances of biased decision-making have led to a growing distrust among consumers who are subject to ML-based decisions. To address this challenge and increase consumer trust, technology that enables public verification of the fairness properties of these…
AI models have become integral to enhancing computing efficiency, productivity, and user experiences. Developing small language models (SLMs) is a key focus, enabling more efficient processing on personal computing devices. The problem addressed by researchers is the high computational demand for AI models, which often require substantial power and resources, limiting their deployment on smaller…
LLMs like GPT-4 excel in language comprehension but struggle with high GPU memory usage during inference, limiting their scalability for real-time applications like chatbots. Existing methods reduce memory by compressing the KV cache but overlook inter-layer dependencies and pre-computation memory demands. Inference memory usage primarily comes from model parameters and the KV cache, with the…
Language models (LMs) are a cornerstone of artificial intelligence research, focusing on the ability to understand and generate human language. Researchers aim to enhance these models to perform various complex tasks, including natural language processing, translation, and creative writing. This field examines how LMs learn, adapt, and scale their capabilities with increasing computational resources. Understanding…
Recent technological advancements in genomics and imaging have resulted in a vast increase in molecular and cellular profiling data, presenting challenges for traditional analysis methods. Modern machine learning, particularly deep learning, offers solutions by handling large datasets to uncover hidden structures and make accurate predictions. This article explores deep learning applications in regulatory genomics and…
The worldwide wearables industry is predicted to grow at a CAGR of 18% by 2026. With the addition of fitness tracking, health monitoring, virtual assistants, and other capabilities, wearable technology has advanced significantly in the last several years. There is still much room for development, but AI is poised to enhance the performance and functionality…