Optimal transport (OT) theory has been been used in machine learning to study and characterize maps that can push-forward efficiently a probability measure onto another. Recent works have drawn inspiration from Brenier’s theorem, which states that when the ground cost is the squared-Euclidean distance, the… Read More »The Monge Gap: A Regularizer to Learn All Transport Maps Apple Machine Learning Research
*= Equal Contributors Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for… Read More »Private Online Prediction from Experts: Separations and Faster Rates Apple Machine Learning Research
m*= Equal Contributors Training stability is of great importance to Transformers. In this work, we investigate the training dynamics of Transformers by examining the evolution of the attention layers. In particular, we track the attention entropy for each attention head during the course of training,… Read More »Stabilizing Transformer Training by Preventing Attention Entropy Collapse Apple Machine Learning Research
We present Spatial LibriSpeech, a spatial audio dataset with over 570 hours of 19-channel audio, first-order ambisonics, and optional distractor noise. Spatial LibriSpeech is designed for machine learning model training, and it includes labels for source position, speaking direction, room acoustics and geometry. Spatial LibriSpeech… Read More »Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning Apple Machine Learning Research
The automotive industry has long pursued the goal of autonomous driving, recognizing its potential to revolutionize transportation and enhance road safety. However, developing autonomous systems that can effectively navigate complex real-world scenarios has proven to be a significant challenge. A cutting-edge generative AI model… Read More »WAYVE Introduces GAIA-1: A New Generative AI Model for Autonomy that Creates Realistic Driving Videos by Leveraging Video, Text, and Action Inputs Niharika Singh Artificial Intelligence Category – MarkTechPost
Large Language Models have shown remarkable performance in a massive range of tasks. From producing unique and creative content and questioning answers to translating languages and summarizing textual paragraphs, LLMs have been successful in imitating humans. Some well-known LLMs like GPT, BERT, and PaLM… Read More »Meet LLM-Blender: A Novel Ensembling Framework to Attain Consistently Superior Performance by Leveraging the Diverse Strengths of Multiple Open-Source Large Language Models (LLMs) Tanya Malhotra Artificial Intelligence Category – MarkTechPost
Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. For provisioning Studio in your AWS account and Region, you first need to create an Amazon SageMaker domain—a… Read More »Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles Ram Vittal AWS Machine Learning Blog
Home Table of Contents Fundamentals of Recommendation Systems Recommendation Engines and Their Types What Are Recommendation Engines? Content-Based Recommendations Collaborative Recommendations Hybrid Recommendations Evaluating Recommendation Systems Root Mean Square Error Precision@K Recall@K Mean Reciprocal Rank Recommendation Techniques Text Mining K-Nearest Neighbor Clustering Matrix Factorization… Read More »Fundamentals of Recommendation Systems Puneet Mangla PyImageSearch
Large language models (LLMs), including GPT-3, PaLM, OPT, BLOOM, and GLM-130B, have greatly pushed the limits of what is possible for computers to comprehend and produce in terms of language. One of the most fundamental language applications, question answering, has been significantly improved due… Read More »A Group of Researchers from China Developed WebGLM: A Web-Enhanced Question-Answering System based on the General Language Model (GLM) Aneesh Tickoo Artificial Intelligence Category – MarkTechPost
Model size and inference workloads have grown dramatically as large diffusion models for image production have become more commonplace. Due to resource limitations, optimizing performance for on-device ML inference in mobile contexts is a delicate balancing act. Due to these models’ considerable memory requirements… Read More »This AI Paper from Google Presents a Set of Optimizations that Collectively Attain Groundbreaking Latency Figures for Executing Large Diffusion Models on Various Devices Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost