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Found 8 papers, 3 papers with code
Evolving Deep Neural Networks
The success of deep learning depends on finding an architecture to fit the task.
Evolutionary Architecture Search For Deep Multitask Networks
Multitask learning, i. e. learning several tasks at once with the same neural network, can improve performance in each of the tasks.
Evolutionary Neural AutoML for Deep Learning
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
Regularized Evolutionary Population-Based Training
This paper presents an algorithm called Evolutionary Population-Based Training (EPBT) that interleaves the training of a DNN's weights with the metalearning of loss functions.
Training Stacked Denoising Autoencoders for Representation Learning
We implement stacked denoising autoencoders, a class of neural networks that are capable of learning powerful representations of high dimensional data.
Designing Counterfactual Generators using Deep Model Inversion
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms.
Asynchronous Evolution of Deep Neural Network Architectures
Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed.
