TikTok Researchers Introduce ‘Depth Anything’: A Highly Practical Solution for Robust Monocular Depth Estimation
Foundational models are large deep-learning neural networks that are used as a starting point to develop effective ML models. They rely on large-scale training data and exhibit exceptional zero/few-shot performance…
This Paper Reveals The Surprising Influence of Irrelevant Data on Retrieval-Augmented Generation RAG Systems’ Accuracy and Future Directions in AI Information Retrieval
In advanced machine learning, Retrieval-Augmented Generation (RAG) systems have revolutionized how we approach large language models (LLMs). These systems extend the capabilities of LLMs by integrating an Information Retrieval (IR)…
This AI Paper from UNC-Chapel Hill Proposes ReGAL: A Gradient-Free Method for Learning a Library of Reusable Functions via Code Refactorization
Optimizing code through abstraction in software development is not just a practice but a necessity. It leads to streamlined processes, where reusable components simplify tasks, increase code readability, and foster…
Microsoft Researchers Introduce StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Natural Language Processing (NLP) is one area where Large transformer-based Language Models (LLMs) have achieved remarkable progress in recent years. Also, LLMs are branching out into other fields, like robotics,…
This AI Paper from CMU and Apple Unveils WRAP: A Game-Changer for Pre-training Language Models with Synthetic Data
Large Language Models (LLMs) have gathered a massive amount of attention and popularity among the Artificial Intelligence (AI) community in recent months. These models have demonstrated great capabilities in tasks…
Meet RAGatouille: A Machine Learning Library to Train and Use SOTA Retrieval Model, ColBERT, in Just a Few Lines of Code
Creating effective pipelines, especially using RAG (Retrieval-Augmented Generation), can be quite challenging in information retrieval. These pipelines involve various components, and choosing the right models for retrieval is crucial. While…
Alibaba Researchers Introduce Mobile-Agent: An Autonomous Multi-Modal Mobile Device Agent
Mobile device agents utilizing Multimodal Large Language Models (MLLM) have gained popularity due to the rapid advancements in MLLMs, showcasing notable visual comprehension capabilities. This progress has made MLLM-based agents…
AIWaves Introduces Weaver: A Family of LLMs Specialized for Writing Endeavors
Large language models (LLMs) have become a prominent force in the rapidly evolving landscape of artificial intelligence. These models, built primarily on Transformer architectures, have expanded AI’s capabilities in understanding…
Google DeepMind Researchers Unveil a Groundbreaking Approach to Meta-Learning: Leveraging Universal Turing Machine Data for Advanced Neural Network Training
Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural…
Researchers from the University of Washington Developed a Deep Learning Method for Protein Sequence Design that Explicitly Models the Full Non-Protein Atomic Context
A team of researchers from the University of Washington has collaborated to address the challenges in the protein sequence design method by using a deep learning-based protein sequence design method,…