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Found 19 papers, 4 papers with code
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
PerfRL: A Small Language Model Framework for Efficient Code Optimization
Code optimization is a challenging task requiring a substantial level of expertise from developers.
Neural Algorithms for Graph Navigation
The application of deep reinforcement learning (RL) to graph learning and meta-learning admits challenges from both topics.
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neural networks for self-driving vehicle control systems. The rapid increase in the consumption of memory and computational resources by these models demands the use of multi-core parallel systems to scale the execution of the complex emerging applications that depend on them.
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
The terms multi-task learning and multitasking are easily confused.
Deep Graph Similarity Learning: A Survey
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search.
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output.
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Our improvements to the pipeline on a single machine provide a 3x overall reconstruction speedup, which allowed us to add algorithmic changes improving image quality.
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers.
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Segmenting Brain Tumors with Symmetry
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task.
Inductive Representation Learning in Large Attributed Graphs
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data.
Revisiting Role Discovery in Networks: From Node to Edge Roles
The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted.
Graphlet Decomposition: Framework, Algorithms, and Applications
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level.
