importpytorch_influence_functionsasptif# Supplied by the user:model=get_my_model()trainloader,testloader=get_my_dataloaders()ptif.init_logging()config=ptif.get_default_config()influences,harmful,helpful=ptif.cal
8 changes: 8 additions & 0 deletions 8 pytorch_influence_functions/calc_influence_function.py Original file line numberDiff line numberDiff line change @@ -365,6 +365,11 @@ def get_dataset_sample_ids_per_class(class_id, num_samples, test_loader,...
In deep learning, several factors influence performance levels. Key considerations include the training speed, effectiveutilization of GPUs, and proficiency in handling extensive models and datasets. PyTorch and TensorFlow use GPU acceleration, utilizingNVIDIA CUDAor AMD ROCm, to boost the efficiency of ...
Soumith, I think that you are probably one of the people who is having the greatest impact on AI today given your influence on the PyTorch ecosystem, and the question I kind of want to start with, as I always do, is the origin story. How did you get into AI in the first place?
# # .. figure:: /_static/img/dcgan_generator.png # :alt: dcgan_generator # # Notice, the how the inputs we set in the input section (*nz*, *ngf*, and # *nc*) influence the generator architecture in code. *nz* is the length # of the z input vector, *ngf* relates to the...
s artificial identity for the purpose of knowingly deceiving a person about the content of the communication. It applies where the person is trying to incentivize a purchase or sale of goods or services in a commercial transaction or to influence voting. No liability attaches, however, if the ...
UsingInternal Influence, one can estimate the integral of gradients along the path from a baseline input to the provided input. This technique is similar to applying integrated gradients, which involves integrating the gradient with regard to the layer (rather than the input). ...
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Inform...
Hyperparameter tuning is the process of selecting the best values for parameters that govern the training of a machine learning model but are not learned from the data itself. These parameters directly influence how the model optimizes and converges. ...
In combination, these effects can influence the performance of a CNN significantly. This is demonstrated in the following by comparing the performance of CNNs that are trained for the same task but use either hexagonal- or square-grid operations on hexagonal or re-sampled data, respectively. For...