Search Results for author: Alex Aiken

Found 14 papers, 3 papers with code

Putting People in Their Place: Affordance-Aware Human Insertion into Scenes

1 code implementation CVPR 2023 Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A. Efros, Krishna Kumar Singh

Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances.

Training with Mixed-Precision Floating-Point Assignments

no code implementations31 Jan 2023 Wonyeol Lee, Rahul Sharma, Alex Aiken

Hence, it is important to use a precision assignment -- a mapping from all tensors (arising in training) to precision levels (high or low) -- that keeps most of the tensors in low precision and leads to sufficiently accurate models.

Image Classification

On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters

no code implementations31 Jan 2023 Wonyeol Lee, Sejun Park, Alex Aiken

For a neural network with bias parameters, we first prove that the incorrect set is always empty.

Programmatic Concept Learning for Human Motion Description and Synthesis

no code implementations CVPR 2022 Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu

We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts.

Optimizing Mixture of Experts using Dynamic Recompilations

no code implementations4 May 2022 Ferdinand Kossmann, Zhihao Jia, Alex Aiken

The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs).

Hierarchical Motion Understanding via Motion Programs

no code implementations CVPR 2021 Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu

We posit that adding higher-level motion primitives, which can capture natural coarser units of motion such as backswing or follow-through, can be used to improve downstream analysis tasks.

Video Editing Video Prediction

Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance

no code implementations15 Aug 2019 Elliott Slaughter, Wei Wu, Yuankun Fu, Legend Brandenburg, Nicolai Garcia, Wilhem Kautz, Emily Marx, Kaleb S. Morris, Wonchan Lee, Qinglei Cao, George Bosilca, Seema Mirchandaney, Sean Treichler, Patrick McCormick, Alex Aiken

We present Task Bench, a parameterized benchmark designed to explore the performance of parallel and distributed programming systems under a variety of application scenarios.

Distributed, Parallel, and Cluster Computing

SPoC: Search-based Pseudocode to Code

1 code implementation NeurIPS 2019 Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy Liang

Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation.

Program Synthesis Translation

Redundancy-Free Computation Graphs for Graph Neural Networks

no code implementations9 Jun 2019 Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken

Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.

Beyond Data and Model Parallelism for Deep Neural Networks

no code implementations14 Jul 2018 Zhihao Jia, Matei Zaharia, Alex Aiken

We also propose FlexFlow, a deep learning framework that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine.

Distributed, Parallel, and Cluster Computing

Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks

no code implementations ICML 2018 Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.

Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

no code implementations14 Feb 2018 Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.

Exploring the Hidden Dimension in Accelerating Convolutional Neural Networks

no code implementations ICLR 2018 Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.

Synthesizing Program Input Grammars

1 code implementation5 Aug 2016 Osbert Bastani, Rahul Sharma, Alex Aiken, Percy Liang

We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program.

Programming Languages

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