Papers
On the Feasibility of Automated Detection of Allusive Text Reuse
Enrique Manjavacas, Brian Long, Mike Kestemont
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Rohin Shah, Noah Gundotra, Pieter Abbeel et al.
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang, Stephan Zheng, Caiming Xiong et al.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines et al.
On the Global Optima of Kernelized Adversarial Representation Learning
Bashir Sadeghi, Runyi Yu, Vishnu Boddeti
On the Hardness of Probabilistic Inference Relaxations
Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi
On the Hardness of Robust Classification
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska et al.
On the Idiosyncrasies of the Mandarin Chinese Classifier System
Shijia Liu, Hongyuan Mei, Adina Williams et al.
On the Impact of the Activation function on Deep Neural Networks Training
Soufiane Hayou, Arnaud Doucet, Judith Rousseau
On the Importance of Audio-Source Separation for Singer Identification in Polyphonic Music
Bidisha Sharma, Rohan Kumar Das, Haizhou Li
On the Importance of Delexicalization for Fact Verification
Sandeep Suntwal, Mithun Paul, Rebecca Sharp et al.
On the Importance of Delexicalization for Fact Verification
Sandeep Suntwal, Mithun Paul, Rebecca Sharp et al.
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Yi Zhu, Benjamin Heinzerling, Ivan Vulić et al.
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Victor Prokhorov, Ehsan Shareghi, Yingzhen Li et al.
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman, Orhan Firat, Mattia A. Di Gangi et al.
On the Inducibility of Stackelberg Equilibrium for Security Games
Qingyu Guo, Jiarui Gan, Fei Fang et al.
On the Inductive Bias of Neural Tangent Kernels
Alberto Bietti, Julien Mairal
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
Aaron Defazio, Leon Bottou
On the (In)fidelity and Sensitivity of Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala et al.
On the Integration of CP-nets in ASPRIN
Mario Alviano, Javier Romero, Torsten Schaub
On the Interaction Effects Between Prediction and Clustering
Matt Barnes, Artur Dubrawski
On the Intrinsic Dimensionality of Image Representations
Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke et al.
On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek, Gabriel Stanovsky, Yichu Zhou et al.