Deep Probabilistic Programming (1701.03757v2)

Abstract: We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.

• The paper introduces Edward, a Turing-complete language that unifies model building with composable inference techniques.
• The paper demonstrates Edward’s seamless integration with TensorFlow, enabling GPU acceleration and distributed training.
• The paper shows benchmark results where Edward is at least 35 times faster than Stan and six times quicker than PyMC3, highlighting its computational advantages.

Overview of "Deep Probabilistic Programming" Paper

This paper introduces Edward, a Turing-complete probabilistic programming language (PPL) designed for deep probabilistic models and efficient inference. The authors propose that Edward's architecture can equip probabilistic programming with the flexibility and computational efficiency akin to traditional deep learning paradigms. Below, we provide an expert summary emphasizing the numerical results, key contributions, and potential implications for AI.

Key Contributions and Insights

Edward defines two compositional representations—random variables and inference—allowing for a unified and integrative framework. This dual representation is critical because it elevates inference to the same level of importance as model building, thus markedly enhancing the flexibility of probabilistic programming. Users can specify generative probabilistic models that are compiled into inference procedures.

1. Flexibility of Inference Methods:
   • Edward supports various compositional inference methods such as point estimation, variational inference, and Markov Chain Monte Carlo (MCMC). This composability permits enhanced experimental design and methodological exploration.
   • By incorporating existing modeling representations into inference tasks, Edward facilitates the construction of sophisticated variational models and the training of generative adversarial networks (GANs).
2. Integration with TensorFlow:
   • Edward is tightly integrated with TensorFlow, enabling users to leverage TensorFlow's computational efficiencies—such as distributed training, parallelism, and Graphics Processing Unit (GPU) support.
3. Benchmark Results:
   • As evidenced by benchmarks on logistic regression tasks, Edward demonstrates significant computational speed advantages: it is at least 35 times faster than Stan and six times faster than PyMC3. Notably, Edward maintains parity with handwritten TensorFlow code, incurring no runtime overhead. This performance is attributable to efficient matrix operations executed on GPUs.

Implications and Future Directions

The development of Edward has important practical and theoretical implications. Practically, researchers can now implement and evaluate complex, probabilistic models with heightened speed and flexibility. Theoretically, Edward's approach may inspire the inception of new PPL designs that harmonize the expressiveness of generative models with scalable inference procedures.

Future Developments in AI:

Edward's architecture opens avenues for exploring novel forms of variational inference and hierarchical generative processes. As the framework supports intricate structures such as variational auto-encoders (VAEs) and Bayesian recurrent neural networks (RNNs), it stands to significantly advance research in probabilistic deep learning. Furthermore, Edward is positioned to address challenges in handling complex, dynamic control flow in probabilistic programs, potentially driving innovation in areas such as autonomous systems and complex decision-making models.

In conclusion, Edward provides a robust, flexible, and efficient platform for deep probabilistic programming, with implications that extend well into future explorations of AI capabilities and applications. The researchers have laid foundational work that bridges the gap between traditional language expressiveness and modern computational efficiency, signifying a pivotal shift in how probabilistic models can be developed and leveraged within the deep learning community.