140 citations · 215 across the 18 of their papers we have counts for
7 papers · 1 filter
Graph Autoencoders with Deconvolutional Networks
Jia Li, Tomas Yu, Da-Cheng Juan +3
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations. In this paper, w…
Adversarial Robustness Across Representation Spaces
Pranjal Awasthi, George Yu, Chun-Sung Ferng +2
Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms ha…
Surprise: Result List Truncation via Extreme Value Theory
Dara Bahri, Che Zheng, Yi Tay +2
Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of…
Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study
Dara Bahri, Yi Tay, Che Zheng +3
Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work i…
BusTr: Predicting Bus Travel Times from Real-Time Traffic
Richard Barnes, Senaka Buthpitiya, James Cook +3
We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public tra…
Choppy: Cut Transformer For Ranked List Truncation
Dara Bahri, Yi Tay, Che Zheng +2
Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the proble…
Reverse Engineering Configurations of Neural Text Generation Models
Yi Tay, Dara Bahri, Che Zheng +3
This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as…