output
20152026
most citedDynamic Memory Networks for Visual and Textual Question Answering

674 citations

Showing 2020Show all

61 papers · 1 filter

cs.CL2020★ 16 cited

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

Xi Victoria Lin, Richard Socher, Caiming Xiong

We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE rep…

cs.LG2020

Communication-Aware Collaborative Learning

Avrim Blum, Shelby Heinecke, Lev Reyzin

Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC le…

cs.LG2020★ 13 cited

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand +6

The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee st…

cs.CL2020★ 3 cited

Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition

Yubei Xiao, Ke Gong, Pan Zhou +3

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulat…

cs.LG2020★ 8 cited

GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning

Govardana Sachithanandam Ramachandran, Ivan Brugere, Lav R. Varshney +1

Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. For example, urban infrastructure networks may enable certai…

cs.LG2020★ 14 cited

Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models

Pascal Sturmfels, Jesse Vig, Ali Madani +1

For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein predict…