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Average Ratings 0 Ratings
Description
Odyssey-3 represents an advanced foundational world model and dynamic system that forecasts the movement and interaction of objects across space while tracking the evolution of various situations over time. Utilizing an autoregressive diffusion transformer, it captures representations related to physics, dynamics, and the principles of cause-and-effect from an extensive collection of visual data, enabling it to simulate environments effectively and develop strategies for managing physical systems. Additionally, Odyssey-3 is capable of creating immersive environments based on user prompts and can instantly predict how these environments shift in response to actions taken by individuals or agents, featuring both first-person and third-person navigation along with flexible camera controls. The extensive world knowledge it acquires can be tailored to various systems by retraining an action decoder or policy using paired data of observations and corresponding actions. Even with limited amounts of task-specific data, Odyssey-3 has demonstrated its utility in controlling robotic arms, powering humanoid robots, operating vehicles, and producing synchronized multi-sensory observations. Furthermore, it is capable of facilitating interactive experiences that enhance user engagement and understanding.
Description
TabFM is an innovative zero-shot foundation model specifically created for handling tabular data, aimed at streamlining classification and regression processes that usually necessitate extensive manual model training, hyperparameter optimization, and tailored feature engineering. By transforming the challenge of tabular prediction into an in-context learning task, TabFM avoids the need to train a new supervised model for every dataset; instead, it consolidates historical training examples and target testing rows into a single cohesive prompt, allowing it to discern the intricate relationships between various columns and rows during inference. Given that tables are inherently two-dimensional and do not rely on a specific order, TabFM employs a hybrid architecture that integrates alternating attention mechanisms for both rows and columns, row compression techniques, and a specialized Transformer designed for in-context learning based on these compressed row embeddings. This sophisticated framework enables the model to effectively capture complex interactions and dependencies among features while maintaining computational efficiency, particularly advantageous for processing larger datasets. Furthermore, this approach not only enhances performance but also significantly reduces the time and resources typically required for model development in tabular data tasks.
API Access
Has API
No
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Odyssey
Founded
2023
Country
United States
Website
odyssey.systems/meet-odyssey-3
Vendor Details
Company Name
Founded
1998
Country
United States
Website
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/
Product Features
Product Features
Alternatives
No Alternatives