Search Results for author:
Found 11 papers, 0 papers with code
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance.
Breaking MLPerf Training: A Case Study on Optimizing BERT
Speeding up the large-scale distributed training is challenging in that it requires improving various components of training including load balancing, communication, optimizers, etc.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition
As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services.
Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition.
Mako: Semi-supervised continual learning with minimal labeled data via data programming
This tool achieves similar performance, in terms of per-task accuracy and resistance to catastrophic forgetting, as compared to fully labeled data.
Data-free mixed-precision quantization using novel sensitivity metric
Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices.
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer
In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks.
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector
In this paper, we revisit the classical bootstrap aggregating approach in the context of modern transfer learning for data-efficient disaster damage detection.
Training Deep Neural Network in Limited Precision
Energy and resource efficient training of DNNs will greatly extend the applications of deep learning.
Quantization for Rapid Deployment of Deep Neural Networks
This paper aims at rapid deployment of the state-of-the-art deep neural networks (DNNs) to energy efficient accelerators without time-consuming fine tuning or the availability of the full datasets.
