Search Results
Evaluating analytic gradients of pulse programs on quantum computers
Parametrized pulse programs running on quantum hardware can be differentiated via the stochastic parameter-shift (SPS) rule.
Quantum Physics
GraphScope Flex: LEGO-like Graph Computing Stack
Furthermore, GraphScope Flex accomplishes up to a 2, 400X performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness.
Distributed, Parallel, and Cluster Computing Databases
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.
On Second-Order Derivatives of Rigid-Body Dynamics: Theory & Implementation
Model-based control for robots has increasingly been dependent on optimization-based methods like Differential Dynamic Programming and iterative LQR (iLQR).
Robotics
HiFrames: High Performance Data Frames in a Scripting Language
Data frames in scripting languages are essential abstractions for processing structured data.
Distributed, Parallel, and Cluster Computing
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library.
Distributed, Parallel, and Cluster Computing
Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs
With the ubiquity of accelerators, such as FPGAs and GPUs, the complexity of high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science.
Programming Languages Distributed, Parallel, and Cluster Computing Performance
Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP).
Robotics Optimization and Control
Finch: Sparse and Structured Tensor Programming with Control Flow
Finch supports a familiar programming language of loops, statements, ifs, breaks, etc., over a wide variety of tensor structures, such as sparsity, run-length-encoding, symmetry, triangles, padding, or blocks.
Mathematical Software
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network
To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way.
Ranked #1 on
Neural Architecture Search
on ImageNet
