Search Results
Manu: A Cloud Native Vector Database Management System
In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance.
Databases
CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition
Facial recognition systems rely on embeddings to represent facial images and determine identity by verifying if the distance between embeddings is below a pre-tuned threshold.
The Sky Above The Clouds
Technology ecosystems often undergo significant transformations as they mature.
Distributed, Parallel, and Cluster Computing
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.
POLCA: Power Oversubscription in LLM Cloud Providers
We propose POLCA, our framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters.
SkyServe: Serving AI Models across Regions and Clouds with Spot Instances
To address this, we propose a simple yet efficient policy, SpotHedge, that leverages spot replicas across different failure domains (e. g., regions and clouds) to ensure availability, lower costs, and high service quality.
Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework
The advancements in cloud-based Large Languages Models (LLMs) have revolutionized AI-assisted programming.
DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network
The average accuracy is 93. 56%, compared with 85. 36% from CFMask.
Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for Foundation Models
The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast valuable data with varied types utilized by these models.
PCPNET: Learning Local Shape Properties from Raw Point Clouds
In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds.
Computational Geometry
