Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.

Description

The Large-Scale Optimizer™ is a collaborative creation by Michael Best, who is a Professor Emeritus in the Department of Combinatorics and Optimization at the University of Waterloo, and Jivendra Kale, the President of Financiometrics Inc. This remarkable quadratic optimizer is designed for the swift construction of long-only, long-short, and market-neutral portfolios that can encompass thousands of assets, allowing for effective risk management in relation to a standard or benchmark portfolio. Additionally, it serves as a tool for asset allocation that employs Markowitz mean-variance analysis principles. This version of the Large-Scale Optimizer™ is an unrestricted edition that can be acquired either as an application or as a subroutine library that can be integrated into your software. Utilizing an advanced active set method, which has been further refined through the implementation of penalty function techniques, the Large-Scale Optimizer™ achieves significant speed enhancements to ensure the attainment of a true global optimal solution for extensive, real-world portfolio optimization challenges, even when variable transaction costs are present. This unique capability makes it an essential tool for financial analysts and portfolio managers seeking to optimize their investment strategies efficiently.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Apple

Founded

1976

Country

United States

Website

github.com/apple/ml-ferret

Vendor Details

Company Name

Financiometrics

Website

www.financiometrics.com

Product Features

Product Features

Financial Risk Management

Compliance Management
Credit Risk Management
For Hedge Funds
Liquidity Analysis
Loan Portfolio Management
Market Risk Management
Operational Risk Management
Portfolio Management
Portfolio Modeling
Risk Analytics Benchmarks
Stress Tests
Value At Risk Calculation

Alternatives

GLM-4.5V Reviews

GLM-4.5V

Zhipu AI

Alternatives

Selene 1 Reviews

Selene 1

atla
SubQ Reviews

SubQ

Subquadratic