output
20152024
most citedFeature relevance quantification in explainable AI: A causal problem

156 citations

Showing 2019 · cs.LGShow all

27 papers · 2 filters

cs.LG20195 cited

What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant

Alejandro Mottini, Amber Roy Chowdhury

A considerable part of the success experienced by Voice-controlled virtual assistants (VVA) is due to the emotional and personalized experience they deliver, with humor being a key…

cs.LG20193 cited

Sampling-Free Learning of Bayesian Quantized Neural Networks

Jiahao Su, Milan Cvitkovic, Furong Huang

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesia…

cs.LG20192 cited

Not All Attention Is Needed: Gated Attention Network for Sequence Data

Lanqing Xue, Xiaopeng Li, Nevin L. Zhang

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms comp…

cs.LG20198 cited

Unbiased Evaluation of Deep Metric Learning Algorithms

Istvan Fehervari, Avinash Ravichandran, Srikar Appalaraju

Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common…

cs.LG20194 cited

Label Dependent Deep Variational Paraphrase Generation

Siamak Shakeri, Abhinav Sethy

Generating paraphrases that are lexically similar but semantically different is a challenging task. Paraphrases of this form can be used to augment data sets for various NLP tasks…

cs.LG20199 cited

Intermittent Demand Forecasting with Deep Renewal Processes

Ali Caner Turkmen, Yuyang Wang, Tim Januschowski

Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between…