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ORCIDJulian Martin Eisenschlos
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- affiliation: Google Research, Switzerland
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2020 – today
- 2024
[c23]Julian Eisenschlos, Hernán Maina, Guido Ivetta, Luciana Benotti:
Selectively Answering Visual Questions. ACL (Findings) 2024: 4219-4229- [c22]Syrine Krichene, Francesco Piccinno, Fangyu Liu, Julian Eisenschlos:
Faithful Chart Summarization with ChaTS-Pi. ACL (1) 2024: 8705-8723 - [c21]Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, Tomas Pfister:
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. ICLR 2024 - [i27]Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, Tomas Pfister:
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. CoRR abs/2401.04398 (2024) - [i26]Mubashara Akhtar, Chenxi Pang, Andreea Marzoca, Yasemin Altun, Julian Martin Eisenschlos:
TANQ: An open domain dataset of table answered questions. CoRR abs/2405.07765 (2024) - [i25]Syrine Krichene, Francesco Piccinno, Fangyu Liu, Julian Martin Eisenschlos:
Faithful Chart Summarization with ChaTS-Pi. CoRR abs/2405.19094 (2024) - [i24]Julian Martin Eisenschlos, Hernán Maina, Guido Ivetta, Luciana Benotti:
Selectively Answering Visual Questions. CoRR abs/2406.00980 (2024) - [i23]Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner
, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey A. Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bosnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier J. Hénaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai:
PaliGemma: A versatile 3B VLM for transfer. CoRR abs/2407.07726 (2024) - [i22]Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister:
TableRAG: Million-Token Table Understanding with Language Models. CoRR abs/2410.04739 (2024) - 2023
- [c20]Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun:
DePlot: One-shot visual language reasoning by plot-to-table translation. ACL (Findings) 2023: 10381-10399 - [c19]Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos:
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering. ACL (1) 2023: 12756-12770 - [c18]Julian Martin Eisenschlos, Jeremy R. Cole, Fangyu Liu, William W. Cohen:
WinoDict: Probing language models for in-context word acquisition. EACL 2023: 94-102 - [c17]Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos, Michael J. Q. Zhang, Eunsol Choi, Bhuwan Dhingra:
DiffQG: Generating Questions to Summarize Factual Changes. EACL 2023: 3080-3093 - [c16]Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick, Julian Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein:
Selectively Answering Ambiguous Questions. EMNLP 2023: 530-543 - [c15]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-Adaptive Prompting. EMNLP 2023: 7437-7462 - [c14]Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova:
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding. ICML 2023: 18893-18912 - [i21]Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos, Michael J. Q. Zhang, Eunsol Choi, Bhuwan Dhingra:
DIFFQG: Generating Questions to Summarize Factual Changes. CoRR abs/2303.00242 (2023) - [i20]Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick, Julian Martin Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein:
Selectively Answering Ambiguous Questions. CoRR abs/2305.14613 (2023) - [i19]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-adaptive Prompting. CoRR abs/2305.14926 (2023) - 2022
- [j1]Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen:
Time-Aware Language Models as Temporal Knowledge Bases. Trans. Assoc. Comput. Linguistics 10: 257-273 (2022) - [c13]Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Martin Eisenschlos:
Leveraging Data Recasting to Enhance Tabular Reasoning. EMNLP (Findings) 2022: 4483-4496 - [c12]Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun:
Table-To-Text generation and pre-training with TabT5. EMNLP (Findings) 2022: 6758-6766 - [c11]Fangyu Liu, Julian Martin Eisenschlos, Jeremy R. Cole, Nigel Collier:
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour. AACL/IJCNLP (2) 2022: 210-220 - [c10]Iulia M. Comsa, Julian Martin Eisenschlos, Srini Narayanan:
MiQA: A Benchmark for Inference on Metaphorical Questions. AACL/IJCNLP (2) 2022: 373-381 - [i18]Julian Martin Eisenschlos, Jeremy R. Cole, Fangyu Liu, William W. Cohen:
WinoDict: Probing language models for in-context word acquisition. CoRR abs/2209.12153 (2022) - [i17]Fangyu Liu, Julian Martin Eisenschlos, Jeremy R. Cole, Nigel Collier:
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour. CoRR abs/2209.12786 (2022) - [i16]Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova:
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding. CoRR abs/2210.03347 (2022) - [i15]Iulia-Maria Comsa, Julian Martin Eisenschlos, Srini Narayanan:
MiQA: A Benchmark for Inference on Metaphorical Questions. CoRR abs/2210.07993 (2022) - [i14]Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun:
Table-To-Text generation and pre-training with TabT5. CoRR abs/2210.09162 (2022) - [i13]Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Martin Eisenschlos:
Leveraging Data Recasting to Enhance Tabular Reasoning. CoRR abs/2211.12641 (2022) - [i12]Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel H. Collier, Julian Martin Eisenschlos:
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering. CoRR abs/2212.09662 (2022) - [i11]Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun:
DePlot: One-shot visual language reasoning by plot-to-table translation. CoRR abs/2212.10505 (2022) - 2021
- [c9]Syrine Krichene, Thomas Müller, Julian Martin Eisenschlos:
DoT: An efficient Double Transformer for NLP tasks with tables. ACL/IJCNLP (Findings) 2021: 3273-3283 - [c8]Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller, William W. Cohen:
MATE: Multi-view Attention for Table Transformer Efficiency. EMNLP (1) 2021: 7606-7619 - [c7]Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan L. Boyd-Graber:
Fool Me Twice: Entailment from Wikipedia Gamification. NAACL-HLT 2021: 352-365 - [c6]Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Martin Eisenschlos:
Open Domain Question Answering over Tables via Dense Retrieval. NAACL-HLT 2021: 512-519 - [c5]Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene:
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training. SemEval@ACL/IJCNLP 2021: 423-430 - [i10]Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Martin Eisenschlos:
Open Domain Question Answering over Tables via Dense Retrieval. CoRR abs/2103.12011 (2021) - [i9]Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene:
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training. CoRR abs/2104.01099 (2021) - [i8]Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan L. Boyd-Graber:
Fool Me Twice: Entailment from Wikipedia Gamification. CoRR abs/2104.04725 (2021) - [i7]Syrine Krichene, Thomas Müller, Julian Martin Eisenschlos:
DoT: An efficient Double Transformer for NLP tasks with tables. CoRR abs/2106.00479 (2021) - [i6]Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen:
Time-Aware Language Models as Temporal Knowledge Bases. CoRR abs/2106.15110 (2021) - [i5]Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller, William W. Cohen:
MATE: Multi-view Attention for Table Transformer Efficiency. CoRR abs/2109.04312 (2021) - 2020
- [c4]Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos:
TaPas: Weakly Supervised Table Parsing via Pre-training. ACL 2020: 4320-4333 - [c3]Julian Martin Eisenschlos, Syrine Krichene, Thomas Müller:
Understanding tables with intermediate pre-training. EMNLP (Findings) 2020: 281-296 - [c2]Sebastian Prillo, Julian Martin Eisenschlos:
SoftSort: A Continuous Relaxation for the argsort Operator. ICML 2020: 7793-7802 - [i4]Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos:
TAPAS: Weakly Supervised Table Parsing via Pre-training. CoRR abs/2004.02349 (2020) - [i3]Sebastian Prillo, Julian Martin Eisenschlos:
SoftSort: A Continuous Relaxation for the argsort Operator. CoRR abs/2006.16038 (2020) - [i2]Julian Martin Eisenschlos, Syrine Krichene, Thomas Müller:
Understanding tables with intermediate pre-training. CoRR abs/2010.00571 (2020)
2010 – 2019
- 2019
- [c1]Julian Eisenschlos, Sebastian Ruder, Piotr Czapla, Marcin Kardas, Sylvain Gugger, Jeremy Howard:
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning. EMNLP/IJCNLP (1) 2019: 5701-5706 - [i1]Julian Eisenschlos, Sebastian Ruder, Piotr Czapla, Marcin Kardas, Sylvain Gugger, Jeremy Howard:
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning. CoRR abs/1909.04761 (2019)
Coauthor Index
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last updated on 2024-11-14 00:57 CET by the dblp team
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