Tianlin Liu, Lyle Ungar and João Sedoc, Continual Learning for Sentence Representations Using Conceptors. NAACL-HLT 2019
Xu He, Tianlin Liu, Fatemeh Hadaeghi, and Herbert Jaeger. Reservoir Transfer on Analog Neuromorphic Hardware. The 9th International IEEE EMBS Conference on Neural Engineering (NER), San Francisco, CA, 2019. 3rd best paper award out of 467 accepted papers. .
Tianlin Liu, Lyle Ungar, and João Sedoc. Unsupervised Post-processing of Word Vectors via Conceptor Negation. The 33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, 2019.
Tianlin Liu and Dae Gwan Lee. Fast Binary Compressive Sensing via $\ell_0$ Gradient Descent. The 5th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa 2018), Siegen, Germany, September 2018.
Tianlin Liu, João Sedoc, and Lyle Ungar. Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors. Biocreative/OHNLP workshop at ACM-BCB 2018, Washington, D.C., USA, August 2018.
Tianlin Liu and Arvid Kappas. Predicting Engagement Breakdown in HRI Using Thin-slices of Facial Expressions. AAAI Workshop on Affective Content Analysis, New Orleans, 2018.
Harnesing Slow Dynamics in Neuromorphic Computation. Master thesis, Department of EE and CS, Jacobs University, 2019.
Discrete Compressive Sensing: Null Space Properties and Convex Optimization Methods. Bachelor thesis, Department of Mathematics, Jacobs University, 2016.
Tianlin Liu . A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values. Technical report.