Animal Shelter Analytics in Practice: The Impact of Shelter Animals Count

Improving Large Language Models with FLAME Large Language Models (LLMs) offer robust natural language understanding and generation capabilities for various tasks, from virtual assistants to data analysis. However, they often struggle with factual accuracy, producing misleading…

Importance of Synthetic Data Generation As the demand for high-quality training data increases, synthetic data generation is crucial for enhancing the performance of large language models (LLMs). Instruction-tuned models are typically used for this purpose but…

This paper, accepted at NeurIPS 2023, investigates removing the trigger phrase requirement from virtual assistant interactions. It proposes integrating ASR system decoder signals with acoustic and lexical inputs into a large language model to achieve more…

MoD-SLAM is a groundbreaking method for Simultaneous Localization And Mapping (SLAM) systems, offering real-time, accurate, and scalable dense mapping using only RGB images. It introduces depth estimation, spatial encoding, and loop closure detection to achieve remarkable…

Practical Solutions and Value of ChatGPT for Businesses Customer Support and Virtual Assistants Utilize ChatGPT-based chatbots for 24/7 customer support, reducing response times and empowering human agents. Content Creation and Copywriting Efficiently generate high-quality content for…

Understanding LLM Inference Challenges Large Language Model (LLM) inference requires a lot of memory and computing power. To solve this, we use model parallelism strategies that share workloads across multiple GPUs. This helps reduce memory issues…

Post-Training for Large Language Models (LLMs) Understanding Post-Training: Post-training enhances LLMs by fine-tuning their performance beyond initial training. This involves techniques like supervised fine-tuning (SFT) and reinforcement learning to meet human needs and specific tasks. The…

Understanding Financial Information Analyzing financial data involves understanding numbers, terms, and organized information like tables. It requires math skills and knowledge of economic concepts. While advanced AI models excel in general reasoning, their effectiveness in finance…

The modern object detection heavily relies on deep learning models trained end-to-end with larger and more diverse datasets. Data augmentation offers a way to boost performance without adding new annotations. AWS AI’s research explores generative data…

The article discusses the use of exponential moving average in time series analysis and its application in approximating parameter changes over time. It explores the motivation behind the method, its formula and mathematical interpretation, and introduces…

Introducing the ‘gpt2-chatbot’: A New Era in AI Artificial intelligence is evolving rapidly, with the emergence of the cutting-edge AI model, ‘gpt2-chatbot’, causing a stir in the AI community. This large language model (LLM) has garnered…

Researchers have discovered 2.2 million new crystals, using GNoME, a deep learning tool that predicts material stability, accelerating discovery time equivalent to 800 years of research.

Advancements in Non-Euclidean Representation Learning Machine learning is evolving beyond traditional methods, exploring more complex data representations. Non-Euclidean representation learning is a cutting-edge field focused on capturing the geometric properties of data through advanced methods like…

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Hugging Face Introduces SmolLM: High-Performance Small Language Models Hugging Face has recently released SmolLM, a family of state-of-the-art small models designed to provide powerful performance in a compact form. The SmolLM models are available in three…

Understanding Retrieval-Augmented Generation (RAG) Systems RAG systems enhance language models by integrating external knowledge. They break documents into smaller parts, called chunks, to improve accuracy and relevance in outputs. This approach is evolving to tackle challenges…

Text-to-image diffusion models have dominated generative tasks by producing high-quality outcomes. Recently, image-to-image transformation tasks have been guided by diffusion models with external image conditions. However, the iterative and time-consuming nature of diffusion models limits their…