Professor Forcing: A New Algorithm for Training Recurrent Networks (1610.09038v1)

Abstract: The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.

• The paper introduces the Professor Forcing algorithm, which bridges the gap between teacher forcing and free-running modes during training.
• It employs adversarial techniques to adjust hidden states, ensuring smoother transitions between training and inference phases.
• The approach improves sequence generation performance and shows promise for applications in language modeling and time series prediction.

Analysis of the Abstract Representation in Academic Papers

In examining the abstract structure of scientific papers, this document serves as a template under the \LaTeX\ class iucr, primarily used for crystallography research. The template emphasizes the layout and content formatting that researchers use to succinctly present the essence of their work in an abstract form.

Structural Components:

The abstract captures the core findings or arguments of the research in a single paragraph. This ensures clarity and brevity while maintaining focus on essential concepts. Typical elements include:

• Title and Authors: Positioned at the top, providing immediate context and ownership.
• Affiliation and Contact Information: Detailed author affiliations ensure that readers can trace the academic lineage or institutional backing, which is valuable for networking and collaboration.
• Keywords: These facilitate electronic searchability and categorization, making the research accessible to relevant audiences.
• References, Figures, and Tables: Integrating only the most pivotal data here restricts content to essentials that directly support the understanding of the abstract.

Numerical and Technical Content:

The specificity in referencing, with numbers in square brackets, underscores the precision needed for academic discourse. The inclusion of figures and tables at precise locations in the text highlights the necessity for immediate and relevant visualization of data.

Implications and Speculations:

• Practical Implications: A well-constructed abstract serves as an efficient dissemination tool, vital in a research ecosystem saturated with reading material. By adopting a consistent format, such standardization aids in peer-review processes and publication procedures.
• Theoretical Implications: Acknowledging the impact of succinct abstracts, one could argue for a shift towards more machine-readable formats that facilitate AI-driven literature review and data extraction processes.
• Future Developments: There is potential to further integrate AI-based tools in the writing and review stages, streamlining content to better suit digital databases and search algorithms.

This template reflects the critical elements in structuring a scientific abstract, ensuring that the crux of the research is communicated effectively. Such clarity in presentation is foundational to advancing scholarly communication, enabling precise data interpretation and facilitating ongoing academic discourse.