Tag: ai/ml

Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performance, training these models is notoriously challenging and expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative training strategy that converts a pre-trained dense CLIP model into a sparse MoE architecture. Through extensive experimentation …

Read more “CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling”

At Google Cloud, we’re committed to providing the most open and flexible AI ecosystem for you to build solutions best suited to your needs. Today, we’re excited to announce our expanded AI offerings with Mistral AI on Google Cloud:  Le Chat Enterprise on Google Cloud Marketplace: An AI assistant that offers enterprise search, agent builders, …

Read more “Mistral AI’s Le Chat Enterprise and Mistral OCR 25.05 model are available on Google Cloud”

With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop …

Read more “SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models”

Today, we’re expanding the choice of third-party models available in Vertex AI Model Garden with the addition of Anthropic’s newest generation of the Claude model family: Claude Opus 4 and Claude Sonnet 4. Both Claude Opus 4 and Claude Sonnet 4 are hybrid reasoning models, meaning they offer modes for near-instant responses and extended thinking …

Read more “Announcing Anthropic’s Claude Opus 4 and Claude Sonnet 4 on Vertex AI”

Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data collection which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot …

Read more “Humanoid Policy ~ Human Policy”

Authors: Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede, Ko-Jen Hsiao, and Justin Basilico Motivation Recommender systems have become essential components of digital services across e-commerce, streaming media, and social networks [1, 2]. At Netflix, these systems drive significant product and business impact by connecting members with relevant content at the right time [3, 4]. While …

Read more “FM-Intent: Predicting User Session Intent with Hierarchical Multi-Task Learning”