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2020 – today
- 2024
[c7]Bill Cai, Clarence Boon Liang Ng, Daniel Liang, Shelvia Hotama:
Low-Cost Generation and Evaluation of Dictionary Example Sentences. NAACL-HLT 2024: 3538-3549- [c6]Sabee Grewal
, Vishnu Iyer, William Kretschmer, Daniel Liang:
Improved Stabilizer Estimation via Bell Difference Sampling. STOC 2024: 1352-1363 - [i8]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Pseudoentanglement Ain't Cheap. CoRR abs/2404.00126 (2024) - [i7]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Agnostic Tomography of Stabilizer Product States. CoRR abs/2404.03813 (2024) - [i6]Vishnu Iyer, Daniel Liang:
Tolerant Testing of Stabilizer States with Mixed State Inputs. CoRR abs/2411.08765 (2024) - 2023
- [j3]Daniel Liang:
Clifford Circuits can be Properly PAC Learned if and only if RP=NP. Quantum 7: 1036 (2023) - [c5]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom. ITCS 2023: 64:1-64:20 - [c4]Daniel Liang, David Liang, Alice Wei, Licheng Ryan Kuo, Shaojie Chang, Marc Jason Pomeroy, Yongfeng Gao:
An investigation on energy spectral information of computed tomography for machine learning in lesion classification. Computer-Aided Diagnosis 2023 - [i5]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Improved Stabilizer Estimation via Bell Difference Sampling. CoRR abs/2304.13915 (2023) - [i4]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates. CoRR abs/2305.13409 (2023) - 2022
- [j2]Aravind Gollakota, Daniel Liang:
On the Hardness of PAC-learning Stabilizer States with Noise. Quantum 6: 640 (2022) - [i3]Daniel Liang:
Clifford Circuits can be Properly PAC Learned if and only if RP=NP. CoRR abs/2204.06638 (2022) - [i2]Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang:
Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom. CoRR abs/2209.14530 (2022) - 2021
- [c3]Ling Chen, David Renshaw, Michael Kellam, Ritaban Dutta, Daniel Liang:
Characterization of Reversibly-Actuating Shape Memory Alloy Foils Produced By Planar Flow Casting. IEEE SENSORS 2021: 1-4 - [i1]Aravind Gollakota, Daniel Liang:
On the Hardness of PAC-learning stabilizer States with Noise. CoRR abs/2102.05174 (2021) - 2020
- [j1]Ritaban Dutta, David Renshaw, Cherry Chen, Daniel Liang:
Machine learning based approach for shape memory polymer behavioural characterization. Array 7: 100036 (2020)
2010 – 2019
- 2019
- [c2]Ritaban Dutta, David Renshaw, Hong Yin, Daniel Liang:
Applying Vision Based Predictive Modelling for Rapid Characterization of Shape Memory Polymer. BigMine@KDD 2019 - 2018
- [c1]Ritaban Dutta, Heiko Mueller, Daniel Liang:
An interactive architecture for industrial scale prediction: Industry 4.0 adaptation of machine learning. SysCon 2018: 1-5
Coauthor Index
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