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Dan Friedman
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2020 – today
- 2024
- [i13]Adithya Bhaskar, Dan Friedman, Danqi Chen:
The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models. CoRR abs/2403.03942 (2024) - [i12]Adithya Bhaskar, Alexander Wettig, Dan Friedman, Danqi Chen:
Finding Transformer Circuits with Edge Pruning. CoRR abs/2406.16778 (2024) - 2023
- [j2]Dan Friedman, Adji Bousso Dieng:
The Vendi Score: A Diversity Evaluation Metric for Machine Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c8]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. ACL (1) 2023: 11289-11310 - [c7]Dan Friedman, Alexander Wettig, Danqi Chen:
Learning Transformer Programs. NeurIPS 2023 - [i11]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. CoRR abs/2305.13299 (2023) - [i10]Dan Friedman, Alexander Wettig, Danqi Chen:
Learning Transformer Programs. CoRR abs/2306.01128 (2023) - [i9]R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths:
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. CoRR abs/2309.13638 (2023) - [i8]Dan Friedman, Andrew K. Lampinen, Lucas Dixon, Danqi Chen, Asma Ghandeharioun:
Interpretability Illusions in the Generalization of Simplified Models. CoRR abs/2312.03656 (2023) - 2022
- [c6]Dan Friedman, Alexander Wettig, Danqi Chen:
Finding Dataset Shortcuts with Grammar Induction. EMNLP 2022: 4345-4363 - [i7]Dan Friedman, Adji Bousso Dieng:
The Vendi Score: A Diversity Evaluation Metric for Machine Learning. CoRR abs/2210.02410 (2022) - [i6]Dan Friedman, Alexander Wettig, Danqi Chen:
Finding Dataset Shortcuts with Grammar Induction. CoRR abs/2210.11560 (2022) - 2021
- [c5]Dan Friedman, Ben Dodge, Danqi Chen:
Single-dataset Experts for Multi-dataset Question Answering. EMNLP (1) 2021: 6128-6137 - [c4]Zexuan Zhong, Dan Friedman, Danqi Chen:
Factual Probing Is [MASK]: Learning vs. Learning to Recall. NAACL-HLT 2021: 5017-5033 - [i5]Zexuan Zhong, Dan Friedman, Danqi Chen:
Factual Probing Is [MASK]: Learning vs. Learning to Recall. CoRR abs/2104.05240 (2021) - [i4]Dan Friedman, Ben Dodge, Danqi Chen:
Single-dataset Experts for Multi-dataset Question Answering. CoRR abs/2109.13880 (2021)
2010 – 2019
- 2019
- [c3]Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R. Fabbri, Irene Li, Dan Friedman, Dragomir R. Radev:
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks. AAAI 2019: 7386-7393 - [c2]Jungo Kasai, Dan Friedman, Robert Frank
, Dragomir R. Radev, Owen Rambow:
Syntax-aware Neural Semantic Role Labeling with Supertags. NAACL-HLT (1) 2019: 701-709 - [i3]Jungo Kasai, Dan Friedman, Robert Frank, Dragomir R. Radev, Owen Rambow:
Syntax-aware Neural Semantic Role Labeling with Supertags. CoRR abs/1903.05260 (2019) - [i2]Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R. Fabbri, Irene Li, Dan Friedman, Dragomir R. Radev:
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks. CoRR abs/1909.01716 (2019) - 2017
- [c1]Dan Friedman, Jungo Kasai, R. Thomas McCoy, Robert Frank, Forrest Davis, Owen Rambow:
Linguistically Rich Vector Representations of Supertags for TAG Parsing. TAG 2017: 122-131 - [i1]Yoel Zeldes, Stavros Theodorakis, Efrat Solodnik, Aviv Rotman, Gil Chamiel, Dan Friedman:
Deep density networks and uncertainty in recommender systems. CoRR abs/1711.02487 (2017)
1990 – 1999
- 1990
- [j1]J. Robinson, Dan Friedman, Martha Steenstrup:
Congestion control in BBN packet-switched networks. Comput. Commun. Rev. 20(1): 76-90 (1990)
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