no code implementations • 4 May 2022 • Rishikesh Magar, Yuyang Wang, Amir Barati Farimani
Machine learning (ML) models have been widely successful in the prediction of material properties.
1 code implementation • 24 Feb 2022 • Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
no code implementations • 18 Feb 2022 • Yuyang Wang, Rishikesh Magar, Chen Liang, Amir Barati Farimani
On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.
1 code implementation • ICLR 2022 • Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
no code implementations • 1 Feb 2022 • Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
no code implementations • 18 Dec 2021 • Ke Alexander Wang, Danielle Maddix, Yuyang Wang
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
no code implementations • 14 Dec 2021 • Danielle C Maddix, Nadim Saad, Yuyang Wang
The transport of traffic flow can be modeled by the advection equation.
1 code implementation • 30 Nov 2021 • Rishikesh Magar, Yuyang Wang, Cooper Lorsung, Chen Liang, Hariharan Ramasubramanian, Peiyuan Li, Amir Barati Farimani
Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
no code implementations • 22 Nov 2021 • Dheeraj Baby, Hilaf Hasson, Yuyang Wang
When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of path variation $V_T$ of the comparator sequence.
no code implementations • 12 Nov 2021 • Youngsuk Park, Danielle Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint learnings over multiple quantile levels.
1 code implementation • NeurIPS 2021 • Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
no code implementations • 3 Jul 2021 • Hao Yen, Chao-Han Huck Yang, Hu Hu, Sabato Marco Siniscalchi, Qing Wang, Yuyang Wang, Xianjun Xia, Yuanjun Zhao, Yuzhong Wu, Yannan Wang, Jun Du, Chin-Hui Lee
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
1 code implementation • 13 Jun 2021 • Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang
Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.
no code implementations • 9 Jun 2021 • Aldebaro Klautau, Pedro Batista, Nuria Gonzalez-Prelcic, Yuyang Wang, Robert W. Heath Jr
The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies.
no code implementations • 18 May 2021 • Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, Hao Wang
This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.
no code implementations • 2 Mar 2021 • Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster
In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset.
no code implementations • 27 Feb 2021 • Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic, Robert W. Heath Jr
We design fully-connected layers to optimize channel acquisition and beam alignment.
1 code implementation • 19 Feb 2021 • Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani
In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).
no code implementations • 13 Feb 2021 • Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
no code implementations • 19 Jan 2021 • Yuyang Wang, Zhonglin Cao, Amir Barati Farimani
Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination.
no code implementations • 12 Jan 2021 • Yuyang Wang, Kenji Shimada, Amir Barati Farimani
Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm.
3 code implementations • 20 Nov 2020 • Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu
While much research on distribution shift has focused on changes in the data domain, our work calls attention to rethink generalization for learning dynamical systems.
no code implementations • 4 Oct 2020 • Ali Caner Turkmen, Tim Januschowski, Yuyang Wang, Ali Taylan Cemgil
Intermittency is a common and challenging problem in demand forecasting.
no code implementations • 11 May 2020 • Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic, Robert W. Heath Jr
Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS.
1 code implementation • 23 Nov 2019 • Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting.
5 code implementations • 12 Jun 2019 • Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
no code implementations • 28 May 2019 • Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part.
1 code implementation • NeurIPS 2018 • Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
no code implementations • 30 Nov 2018 • Danielle C. Maddix, Yuyang Wang, Alex Smola
A large collection of time series poses significant challenges for classical and neural forecasting approaches.
no code implementations • 7 Dec 2017 • Lucas Roberts, Leo Razoumov, Lin Su, Yuyang Wang
Moreover, we show that the Gini regularized OT problem converges to the classical OT problem, when the Gini regularized problem is considered as a function of {\lambda}, the regularization parame-ter.
no code implementations • 22 Sep 2017 • Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
We present a scalable and robust Bayesian inference method for linear state space models.
no code implementations • 5 Mar 2012 • Yuyang Wang, Roni Khardon, Pavlos Protopapas
The paper applies this framework for data where each task is a phase-shifted periodic time series.