Nvidia corporation (20240127041). CONVOLUTIONAL STRUCTURED STATE SPACE MODEL simplified abstract
Contents
- 1 CONVOLUTIONAL STRUCTURED STATE SPACE MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONVOLUTIONAL STRUCTURED STATE SPACE MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the convSSM compare to existing state-of-the-art models in terms of computational efficiency?
- 1.11 Are there any limitations or constraints in the implementation of the convSSM in real-world applications?
- 1.12 Original Abstract Submitted
CONVOLUTIONAL STRUCTURED STATE SPACE MODEL
Organization Name
Inventor(s)
Jimmy Smith of Santa Clara CA (US)
Wonmin Byeon of Santa Cruz CA (US)
Shalini De Mello of San Francisco CA (US)
CONVOLUTIONAL STRUCTURED STATE SPACE MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127041 titled 'CONVOLUTIONAL STRUCTURED STATE SPACE MODEL
Simplified Explanation
The patent application describes a convolutional structured state space model (convSSM) that utilizes a tensor-structured state with a continuous-time parameterization and linear state updates. This model allows for efficient parallelization across spatiotemporal sequences, effectively modeling long-range dependencies and enabling fast autoregressive generation.
- The convSSM has a tensor-structured state with continuous-time parameterization and linear state updates.
- It allows for parallel scans for subquadratic parallelization across spatiotemporal sequences.
- The model effectively captures long-range dependencies.
- When followed by a nonlinear operation, it forms a spatiotemporal layer (convS5) that does not require compressing frames into tokens.
- It provides an unbounded context and enables fast autoregressive generation.
Potential Applications
The technology could be applied in:
- Video analysis and processing
- Speech recognition systems
- Time series forecasting
Problems Solved
The technology addresses the following issues:
- Efficient modeling of long-range dependencies
- Parallelization across spatiotemporal sequences
- Fast autoregressive generation
Benefits
The technology offers the following benefits:
- Unbounded context modeling
- Efficient parallelization
- Fast autoregressive generation
Potential Commercial Applications
The technology could be commercially applied in:
- Video editing software
- Speech-to-text transcription services
- Financial forecasting tools
Possible Prior Art
One possible prior art for this technology could be:
- Existing convolutional neural network models for sequence processing.
Unanswered Questions
How does the convSSM compare to existing state-of-the-art models in terms of computational efficiency?
The article does not provide a direct comparison with existing models in terms of computational efficiency. Further research or experimentation may be needed to address this question.
Are there any limitations or constraints in the implementation of the convSSM in real-world applications?
The article does not mention any specific limitations or constraints in the implementation of the convSSM in real-world applications. Further analysis or case studies may be required to explore this aspect.
Original Abstract Submitted
systems and methods are disclosed related to a convolutional structured state space model (convssm), which has a tensor-structured state but a continuous-time parameterization and linear state updates. the linearity may be exploited to use parallel scans for subquadratic parallelization across the spatiotemporal sequence. the convssm effectively models long-range dependencies and, when followed by a nonlinear operation forms a spatiotemporal layer (convs5) that does not require compressing frames into tokens, can be efficiently parallelized across the sequence, provides an unbounded context, and enables fast autoregressive generation.
