Avanti Shrikumar

According to our database1, Avanti Shrikumar authored at least 13 papers between 2016 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2021
Towards a Better Understanding of Reverse-Complement Equivariance for Deep Learning Models in Genomics.
Proceedings of the Machine Learning in Computational Biology Meeting, 2021

Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics.
Proceedings of the Machine Learning in Computational Biology Meeting, 2021

2020
Interpretable machine learning for scientific discovery in regulatory genomics.
PhD thesis, 2020

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks.
CoRR, 2019

GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.
Bioinform., 2019

Suggested Best Practices for Interpreting Deep Learning Models via Input-Level Importance Scores.
Proceedings of the Symposium Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness co-located with Association for the Advancement of Artificial Intelligence 2019 Spring Symposium (AAAI-Spring Symposium 2019), 2019

2018
TF-MoDISco v0.4.4.2-alpha: Technical Note.
CoRR, 2018

Computationally Efficient Measures of Internal Neuron Importance.
CoRR, 2018

Learning to Abstain via Curve Optimization.
CoRR, 2018

2017
Learning Important Features Through Propagating Activation Differences.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.
CoRR, 2016


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