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Ayesha Bajwa
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2020 – today
- 2023
- [c8]Ayesha Bajwa, Ruchir Rastogi, Pooja Kathail, Richard W. Shuai, Nilah Ioannidis:
Characterizing uncertainty in predictions of genomic sequence-to-activity models. MLCB 2023: 279-297 - 2020
- [c7]Bertie Ancona, Ayesha Bajwa, Nancy A. Lynch, Frederik Mallmann-Trenn:
How to Color a French Flag - Biologically Inspired Algorithms for Scale-Invariant Patterning. LATIN 2020: 413-424
2010 – 2019
- 2019
- [c6]Ayesha Bajwa, Erik Hemberg, Ana Bell, Una-May O'Reilly:
Student Code Trajectories in an Introductory Programming MOOC. L@S 2019: 33:1-33:4 - [c5]Jitesh Maiyuran, Ayesha Bajwa, Ana Bell, Erik Hemberg, Una-May O'Reilly:
How Student Background and Topic Impact the Doer Effect in Computational Thinking MOOCs. LWMOOCS 2019: 47-52 - [c4]Ayesha Bajwa, Ana Bell, Erik Hemberg, Una-May O'Reilly:
Analyzing Student Code Trajectories in an Introductory Programming MOOC. LWMOOCS 2019: 53-58 - [c3]Erik Hemberg, Sagar Biswas, Ayesha Bajwa, Nancy Law, Una-May O'Reilly:
Categorizing Resources and Learners for a Finer-Grained Analysis of MOOC Viewing & Doing. LWMOOCS 2019: 116-121 - [c2]Bertie Ancona, Ayesha Bajwa, Nancy A. Lynch, Frederik Mallmann-Trenn:
How to Color a French Flag - Biologically Inspired Algorithms for Scale-Invariant Patterning. SIROCCO 2019: 327-331 - [i2]Alberto Ancona, Ayesha Bajwa, Nancy A. Lynch, Frederik Mallmann-Trenn:
How to Color a French Flag-Biologically Inspired Algorithms for Scale-Invariant Patterning. CoRR abs/1905.00342 (2019) - 2018
- [c1]Leilani H. Gilpin, David Bau
, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal
:
Explaining Explanations: An Overview of Interpretability of Machine Learning. DSAA 2018: 80-89 - [i1]Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal:
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. CoRR abs/1806.00069 (2018)
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last updated on 2024-05-14 20:25 CEST by the dblp team
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