Very deep convolutional neural networks offer excellent recognition results, yet their computational
expense limits their impact for many real-world applications. We introduce BlockDrop…

… -consumption inference, that … dynamic inference (DDI) framework that highlights the following
aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference …

An abstract type groups variables that are used for related purposes in a program. We
describe a dynamic unification-based analysis for inferring abstract types. Initially, each run-time …

… Unlike traditional static one-size-fits-all methods, our proposed fully dynamic inference …
to low computation cost during inference. Furthermore, our dynamic inference approach is …

… We consider a model for dynamic uncertain processes that … of the general class of dynamic
inference models range from … process; we call this process ‘Dynamic Inference.’ For a military …

… of making the inference. More flexible access control is possible when inferences are prevented
… We demonstrate that access control can be made sufficiently dynamic to ensure easy …

… In this work, we present Neural Interpreters, an architecture that factorizes inference in a
self-attention network as a system of modules, which we call functions. Inputs to the model are …

… However, it has been appreciated that dynamic progressions … the same dynamic process,
and many dynamic processes can … useful current methods for dynamic inference might be more …

… the Dynamic Inference Network to address the problems of inference on a predefined graph
and inference in a … More challenging tasks and efficient inference models are left for future ex…

… dynamic inference idea to improve inference efficiency by leveraging the variation in the
distinguishability of different videos. The dynamic inference … , whenever the inference process …