End-to-end object detection with transformers.

The former task requires the model to learn to reason over a given image following program guidance.

Lacking closed form expressions for the context vector, we use numerical integration: we prove exponential convergence for both families.

However, such methods are usually sensitive to environmental dynamics-irrelevant information, e.g., white-noise. To handle such dynamics-irrelevant information, we propose a Dynamic Bottleneck (DB) model, which attains a dynamics-relevant representation based on the information-bottleneck principle.

We initiate a formal study of reproducibility in optimization. We define a quantitative measure of reproducibility of optimization procedures in the face of noisy or error-prone operations such as inexact or stochastic gradient computations or inexact initialization.