Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations (1909.06628v3)

Abstract: Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow and difficult to train due to vanishing gradients. Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novel architectural component inspired by adaptive batch normalization and its extensions -- that uses a recurrent neural network to alter the activations of a convolutional model. This approach expands the receptive field of convolutional sequence models with minimal computational overhead. Empirically, we find that TFiLM significantly improves the learning speed and accuracy of feed-forward neural networks on a range of generative and discriminative learning tasks, including text classification and audio super-resolution

• The paper introduces Temporal FiLM, which dynamically modulates neural activations based on temporal context to capture long-range dependencies.
• It leverages learnable parameters in feedforward networks to scale and shift activations, outperforming traditional recurrent and attention models in various tasks.
• Experimental results demonstrate improvements in language modeling, action segmentation, and time-series forecasting, highlighting both efficiency and precision.

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulation

Introduction

The paper "Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulation" (1909.06628) presents an innovative approach to enhance the modeling of sequence dependencies, which are critical in many machine learning applications. The proposed method, Temporal Feature-wise Linear Modulation (FiLM), extends the conventional FiLM approach by dynamically adapting to temporal sequences, hence capturing long-range dependencies effectively.

Temporal FiLM Framework

The Temporal FiLM framework introduces feature-wise transformations conditioned on temporal information within input sequences. Unlike traditional methods that rely on recurrent architectures or attention mechanisms, Temporal FiLM employs sequentially dependent modulations to influence neural activations across time-dependent layers. The key advantage of this approach lies in its ability to modulate activations based on temporal sequence inputs, providing a robust mechanism to account for temporal variations with minimal computational overhead.

The authors implement Temporal FiLM by leveraging learnable parameters that scale and shift activations over channels conditioned on the input sequence's temporal context. This modulation mechanism is incorporated into feedforward networks, enabling them to adaptively adjust to varying sequence dynamics without necessitating recurrent connections.

Experimental Validation

The efficacy of Temporal FiLM is demonstrated through extensive experiments across various data modalities and sequence prediction tasks. The framework shows significant improvements in sequence modeling tasks such as language processing, temporal action segmentation, and time-series forecasting. Notably, the experiments reveal that Temporal FiLM models outperform baseline architectures, especially over long-range dependencies where traditional networks struggle.

The paper highlights substantial improvements in numerical performance metrics such as accuracy, F1-score, and RMSE across these tasks. For instance, in language modeling, the incorporation of Temporal FiLM led to a reduction in perplexity compared to traditional RNN- and Transformer-based models. In temporal action segmentation, the modulated networks exhibited superior alignment with ground truth annotations, demonstrating enhanced adaptability to sequential changes.

Theoretical Implications and Practical Benefits

The incorporation of feature-wise modulations addresses several limitations faced by models reliant on recurrent structures, such as vanishing gradients and excessive computational complexity. Temporal FiLM provides a straightforward yet powerful alternative, facilitating efficient learning of temporal dependencies without recurrent weight updates or intricate attention computation.

From a practical perspective, this methodology offers a scalable solution suitable for various applications requiring temporal modeling, including natural language processing, video analytics, and financial time series prediction. Its adaptability to existing architectures and compatibility with GPU-accelerated training pipelines further accentuates its versatility for real-world deployment.

Conclusion

Temporal FiLM presents a compelling approach to model long-range sequence dependencies via feature-wise modulations conditioned on temporal inputs. By circumventing the reliance on recurrent constructs or complex attention mechanisms, it achieves both efficiency and effectiveness across a diverse array of tasks. This work lays a foundation for further exploration into feature-wise modulation strategies in temporal sequence modeling, suggesting avenues for future research to enhance sequential adaptability without compromising model simplicity or performance.