A latent dynamics model for schedule trajectories in TVM AutoScheduler finds programs with 1.37x better GPU latency than Ansor using the same 64 trials and matches 10K-trial Ansor with 10x fewer measurements.
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
15 Pith papers cite this work. Polarity classification is still indexing.
15
Pith papers citing it
abstract
MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.
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representative citing papers
J-LAW introduces a coupled latent factor graph that jointly optimizes metric poses, latent states, and landmark embeddings to produce maps that are both metric and actionable for planning.
DGL is a graph-centric library that optimizes GNNs via generalized sparse tensor operations, transparent graph-based optimizations, and framework-neutral design, claiming superior speed and memory use over other GNN frameworks.
Proposal-level spatio-temporal context aggregation for video object detection achieves 80.3% mAP on ImageNet VID, improving Faster R-CNN baseline by 5.8%.
MLfabric accelerates distributed ML training up to 3X by managing communication patterns through ordering, in-network aggregation, and proactive replication of updates.
MONAI is a community-supported PyTorch framework that extends deep learning to medical data with domain-specific architectures, transforms, and deployment tools.
Encoder-decoder model with multi-task learning on a low-dimensional latent space improves dysarthria detection accuracy and enables generation of more fluent speech.
MediaPipe is a new open-source framework that lets developers assemble, prototype, and deploy ML-based perception pipelines across platforms with reproducible performance measurements.
Modifications to single-step adversarial training based on empirical properties of iterative methods improve accuracy by up to 16.93% against iterative attacks while reducing training cost by 28.75%.
Authors outline a preliminary CNN framework for liver lesion detection in CT images that adds image processing, region proposal, registration and classification steps to handle 3D medical data.
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
NumPy provides array programming tools that form the foundation of the scientific Python ecosystem and enable data analysis across many disciplines.
A survey introducing Bayesian Neural Networks and comparing approximate inference methods to enable uncertainty quantification in neural network predictions.
citing papers explorer
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Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search
A latent dynamics model for schedule trajectories in TVM AutoScheduler finds programs with 1.37x better GPU latency than Ansor using the same 64 trials and matches 10K-trial Ansor with 10x fewer measurements.
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J-LAW: Joint Localization and Actionable World Modeling via Coupled Latent Factor Graphs
J-LAW introduces a coupled latent factor graph that jointly optimizes metric poses, latent states, and landmark embeddings to produce maps that are both metric and actionable for planning.
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Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
DGL is a graph-centric library that optimizes GNNs via generalized sparse tensor operations, transparent graph-based optimizations, and framework-neutral design, claiming superior speed and memory use over other GNN frameworks.
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Object Detection in Video with Spatial-temporal Context Aggregation
Proposal-level spatio-temporal context aggregation for video object detection achieves 80.3% mAP on ImageNet VID, improving Faster R-CNN baseline by 5.8%.
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Network-accelerated Distributed Machine Learning Using MLFabric
MLfabric accelerates distributed ML training up to 3X by managing communication patterns through ordering, in-network aggregation, and proactive replication of updates.
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MONAI: An open-source framework for deep learning in healthcare
MONAI is a community-supported PyTorch framework that extends deep learning to medical data with domain-specific architectures, transforms, and deployment tools.
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
Encoder-decoder model with multi-task learning on a low-dimensional latent space improves dysarthria detection accuracy and enables generation of more fluent speech.
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MediaPipe: A Framework for Building Perception Pipelines
MediaPipe is a new open-source framework that lets developers assemble, prototype, and deploy ML-based perception pipelines across platforms with reproducible performance measurements.
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Using Intuition from Empirical Properties to Simplify Adversarial Training Defense
Modifications to single-step adversarial training based on empirical properties of iterative methods improve accuracy by up to 16.93% against iterative attacks while reducing training cost by 28.75%.
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Delving Deep into Liver Focal Lesion Detection: A Preliminary Study
Authors outline a preliminary CNN framework for liver lesion detection in CT images that adds image processing, region proposal, registration and classification steps to handle 3D medical data.
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A Survey of Large Language Models
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.
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A Comprehensive Overview of Large Language Models
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
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Array Programming with NumPy
NumPy provides array programming tools that form the foundation of the scientific Python ecosystem and enable data analysis across many disciplines.
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Bayesian Neural Networks: An Introduction and Survey
A survey introducing Bayesian Neural Networks and comparing approximate inference methods to enable uncertainty quantification in neural network predictions.
- An Adaptive Decentralized Quasi-Newton Method with Stepsizes Independent of the Local-Update Budget