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Found 32 papers, 10 papers with code
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Machine learning (ML) models have been widely successful in the prediction of material properties.
Robust Probabilistic Time Series Forecasting
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.
Graph-Relational Domain Adaptation
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.
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow
The transport of traffic flow can be modeled by the advection equation.
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
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.
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
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.
Deep Explicit Duration Switching Models for Time Series
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.
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
Correcting Exposure Bias for Link Recommendation
Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning
The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies.
Zero-Shot Recommender Systems
This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.
Variance Reduced Training with Stratified Sampling for Forecasting Models
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.
Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems
We design fully-connected layers to optimize channel acquisition and beam alignment.
Molecular Contrastive Learning of Representations via Graph Neural Networks
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).
Domain Adaptation for Time Series Forecasting via Attention Sharing
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination.
Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization
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.
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
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.
Intermittent Demand Forecasting with Renewal Processes
Intermittency is a common and challenging problem in demand forecasting.
Site-specific online compressive beam codebook learning in mmWave vehicular communication
Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS.
Intermittent Demand Forecasting with Deep Renewal Processes
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting.
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Deep Factors for Forecasting
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.
Deep State Space Models for Time Series Forecasting
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
Deep Factors with Gaussian Processes for Forecasting
A large collection of time series poses significant challenges for classical and neural forecasting approaches.
Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting
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.
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
We present a scalable and robust Bayesian inference method for linear state space models.
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
The paper applies this framework for data where each task is a phase-shifted periodic time series.
