29 citations · 65 across the 11 of their papers we have counts for
5 papers · 1 filter
Substance or Style: What Does Your Image Embedding Know?
Cyrus Rashtchian, Charles Herrmann, Chun-Sung Ferng +5
Probes are small networks that predict properties of underlying data from embeddings, and they provide a targeted, effective way to illuminate the information contained in embeddin…
CARLS: Cross-platform Asynchronous Representation Learning System
Chun-Ta Lu, Yun Zeng, Da-Cheng Juan +13
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge make…
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…
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…
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
Benjamin Spector, Ravi Kumar, Andrew Tomkins
We propose improving the privacy properties of a dataset by publishing only a strategically chosen "core-set" of the data containing a subset of the instances. The core-set allows…