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Found 8 papers, 2 papers with code
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
We demonstrate that our method attains up to 6. 8x reduction in inference time with <1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT.
GREED: A Neural Framework for Learning Graph Distance Functions
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
Efficient Inferencing of Compressed Deep Neural Networks
Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on cloud.
Effective Elastic Scaling of Deep Learning Workloads
We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs.
Scheduling Resources for Executing a Partial Set of Jobs
In this paper, we consider the problem of choosing a minimum cost set of resources for executing a specified set of jobs.
Data Structures and Algorithms
Efficient Scaling of Dynamic Graph Neural Networks
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems.
NeuroSED: Learning Subgraph Similarity via Graph Neural Networks
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
Compression of Deep Neural Networks by combining pruning and low rank decomposition
Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on the cloud.
