Ashish Agarwal's scientific contributions

Publications (5)

Preprint
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 provided on top of TensorFlow, and used for applications ranging from auto-batching and per-example gradients, to jacobian computation, optimized map functions and input pip...
Preprint
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinde...
Article
Full-text available
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hund...
Technical Report
TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of...

Citations

... A computationally efficient optimization is performed using a surrogate model of the MSKM with as inputs the ligament stiffness, reference strain and attachment positions, and as outputs the TF-kinematics and ligament strains. As surrogate modeling technique, an artificial neural network (ANN) is used, which is implemented using Tensorflow 2.4.1 [25]. For further details, we refer to the study of Bartsoen et al. [26]. ...
... example, can the problem be learned best with a multilayer perceptron (MLP) or a longterm-short-term memory network (LSTM)? 1 After a type of topology has been selected, for example MLP, the precise shape of that topology still needs to be determined and justified, e.g. the number of layers and neurons needs to be specified as hyperparameters as well as the activation functions. In a last step, the networks weights have to be chosen. ...
... According to this, if a sentence length is less than the maximum length, prepadding is used, and if the sentence is longer than the maximum length, pruning is done at the beginning. For experiment purposes, a well-known python library (Keras 2015) was used with tensorFlow Abadi et al. (2016) as a backend, and scikit-learn library Pedregosa et al. (2011) is used for machine learning models. We performed 5-fold cross-validation on the training dataset and evaluated the final model on the test datasets. ...