Dynamic Neural Network with Hierarchical Gating for Multi-Task Learning
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Solution Overview
Problem
Traditional multi-task learning approaches face challenges in scalability and efficiency due to fixed parameter sharing patterns, which can lead to negative transfer and increased computational costs as the number of tasks grows, limiting generalization and inference speed.
Innovation Solution
The implementation of a dynamic neural network that conditionally activates layers based on task type and input instance features using a hierarchical gating policy, combining task-specific and instance-specific policies to determine execution paths at inference time, allowing for flexible parameter sharing and reduced computational footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hard parameter sharing is used to train a single network for multiple tasks, then parameter efficiency and training time are improved, but the architecture becomes restrictive and susceptible to negative transfer
Solution Approach 1:
The patent implements dynamic neural networks where the architecture can change at inference time based on the input instance. Gating units dynamically select which layers to activate, allowing the network to adapt its structure for different tasks and inputs, thus resolving the contradiction between fixed architecture efficiency and adaptive flexibility
Solution Approach 2:
The network is segmented into multiple layers with gating units that can independently control activation. This segmentation allows selective activation of task-relevant layers while keeping other layers dormant, providing both efficiency (by activating only necessary layers) and flexibility (by allowing different layer combinations for different tasks)
2Productivity
If hard parameter sharing is used with a shared backbone, then parameter efficiency is improved, but the network is susceptible to negative transfer where some tasks are optimized at the expense of others
Solution Approach 1:
The dynamic gating mechanism allows the network to selectively activate layers based on task requirements, preventing negative transfer by ensuring that task-specific layers are activated only when needed for their respective tasks, while shared layers are activated based on their relevance to the current task
Solution Approach 2:
Different layers are assigned different qualities or specializations - some layers are more suitable for certain tasks than others. The gating units enable local quality optimization by activating the most appropriate layers for each specific task, thus improving both parameter efficiency and task performance balance
3Adaptability or versatility
If soft parameter sharing is used with multiple task-specific backbones, then adaptability is improved, but scalability becomes challenging as the network grows proportionally with the number of tasks
Solution Approach 1:
The patent creates a universal network backbone that can serve multiple tasks through dynamic layer activation. Instead of having separate backbones for each task, a single backbone with gated layers can adapt to different tasks by selectively activating relevant layers, thus achieving task-specific representation without proportional network growth
Solution Approach 2:
The dynamic gating mechanism allows the same network structure to serve multiple tasks by changing which layers are active at inference time. This dynamic reconfiguration enables one network to replace multiple task-specific networks, improving scalability while maintaining adaptability
4Measurement precision
If all layers are activated for every input, then processing accuracy is improved, but inference time increases
Solution Approach 1:
Instead of activating all layers for every input (excessive action), the gating units enable partial activation of only the necessary layers for each specific input and task. This partial action maintains prediction accuracy for the given input while significantly reducing inference time by skipping irrelevant layers
Solution Approach 2:
The dynamic layer activation adapts the network depth and complexity based on the specific input characteristics and task requirements. For simple inputs or straightforward tasks, fewer layers are activated, reducing inference time. For complex inputs requiring detailed analysis, more layers are activated to maintain prediction accuracy
Data Source
AI summary
Disclosed are systems, methods, and devices for computing an action for an automated agent. A neural network configured for deep multi-task learning is provided. Each of a subset of layers of the neural network is connected with a respective gating unit configured for dynamically activating or deactivating the respective layer of the neural network. The method includes: receiving, via a communication interface, input data associated with a task type; selecting, from a plurality of layers of a neural network, a subset of layers based on at least the task type; dynamically activating, based on the input data, at least one layer of the subset of layers; and generating an action signal based on a forward pass of the neural network using the dynamically activated at least one layer of the neural network.