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Found 8 papers, 4 papers with code
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program.
Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive.
Multiplying Matrices Without Multiplying
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning.
Causally motivated Shortcut Removal Using Auxiliary Labels
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
Better Aggregation in Test-Time Augmentation
In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address these shortcomings.
What is the State of Neural Network Pruning?
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years.
Multiple Instance Learning for ECG Risk Stratification
We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal.
Jiffy: A Convolutional Approach to Learning Time Series Similarity
Computing distances between examples is at the core of many learning algorithms for time series.
