9.7k citations · 9.8k across the 4 of their papers we have counts for
4 papers
Auto-Vectorizing TensorFlow Graphs: Jacobians, Auto-Batching And Beyond
Ashish Agarwal, Igor Ganichev
We propose a static loop vectorization optimization on top of high level dataflow IR used by frameworks like TensorFlow. A new statically vectorized parallel-for abstraction is pro…
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos +8
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. Tensor…
Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal +2
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature…
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi, Ashish Agarwal, Paul Barham +37
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed…