3.4k citations · 4.6k across the 38 of their papers we have counts for
13 papers · 1 filter
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
Wei Qian, Chenxu Zhao, Yangyi Li +3
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on po…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer
Rui Feng, Huan Tran, Aubrey Toland +4
Polymer simulation with both accuracy and efficiency is a challenging task. Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
A Survey on Programmatic Weak Supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu +2
Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved…
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models di…
A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
Wanzheng Zhu, Chao Zhang, Shuochao Yao +2
We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user's activity. E…
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Yue Yu, Kexin Huang, Chao Zhang +3
Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning…
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Qi Zhu, Carl Yang, Yidan Xu +3
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started…
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang
Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scal…
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
Shi Zhi, Liyuan Liu, Yu Zhang +4
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead o…
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang +5
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, an…
RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Shuochao Yao, Yiran Zhao, Huajie Shao +4
Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ub…