Papers
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu, Daniel Worrall
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Lingbing Guo, Zequn Sun, Wei Hu
Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans et al.
Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel et al.
Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum et al.
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi et al.
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld, Meirav Galun, Ronen Basri et al.
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang, Jia Deng
Learning to Route in Similarity Graphs
Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin et al.
Learning to select for a predefined ranking
Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev et al.
Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang et al.
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
Yanyao Shen, Sujay Sanghavi
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu et al.
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
Zhaohui Yang, Yunhe Wang, Chuanjian Liu et al.
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
Giulia Luise, Dimitrios Stamos, Massimiliano Pontil et al.
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee et al.
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei et al.
Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations
Kubilay Atasu, Thomas Mittelholzer
Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song et al.
LIT: Learned Intermediate Representation Training for Model Compression
Animesh Koratana, Daniel Kang, Peter Bailis et al.
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield et al.
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
Tahrima Rahman, Shasha Jin, Vibhav Gogate
Lorentzian Distance Learning for Hyperbolic Representations
Marc Law, Renjie Liao, Jake Snell et al.
Loss Landscapes of Regularized Linear Autoencoders
Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva et al.
Lossless or Quantized Boosting with Integer Arithmetic
Richard Nock, Robert Williamson