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Found 47 papers, 12 papers with code
Urban Regional Function Guided Traffic Flow Prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest.
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment.
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization
When the initial policy is consistent, under some mild conditions, our method will yield a policy whose value converges to the optimal one at a faster rate than the initial policy, achieving the desired ``value enhancement" property.
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Owing to its generalization, MixDA has penetrated a variety of applications which are also completely reviewed in this work.
Predicting Human Mobility via Self-supervised Disentanglement Learning
However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions.
Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs
Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns.
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems
Numerical reasoning over natural language has been a long-standing goal for the research community.
Evolving Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.
Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal
This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations.
Gap Minimization for Knowledge Sharing and Transfer
Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e. g., $\mathcal{H}$-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees.
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc.
Rate-Optimal Subspace Estimation on Random Graphs
We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph.
Directional Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
CCGL: Contrastive Cascade Graph Learning
Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.
Ranked #1 on
Information Cascade Popularity Prediction
on Weibo
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address.
Multi-task Learning by Leveraging the Semantic Information
To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions.
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.
Optimization and Control Applications
Embedding Symbolic Temporal Knowledge into Deep Sequential Models
However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction.
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs
Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines.
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs.
Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.
Domain Generalization via Optimal Transport with Metric Similarity Learning
Previous domain generalization approaches mainly focused on learning invariant features and stacking the learned features from each source domain to generalize to a new target domain while ignoring the label information, which will lead to indistinguishable features with an ambiguous classification boundary.
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC).
Interpreting Twitter User Geolocation
Identifying user geolocation in online social networks is an essential task in many location-based applications.
Discriminative Active Learning for Domain Adaptation
To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation.
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.
Metric learning by Similarity Network for Deep Semi-Supervised Learning
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning.
A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks.
Relational State-Space Model for Stochastic Multi-Object Systems
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.
Deep Active Learning: Unified and Principled Method for Query and Training
In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning.
A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model
Let $\mathbf{x}_j = \mathbf{\theta} + \mathbf{\epsilon}_j$, $j=1,\dots, n$ be i. i. d.
LSTOD: Latent Spatial-Temporal Origin-Destination prediction model and its applications in ride-sharing platforms
Origin-Destination (OD) flow data is an important instrument in transportation studies.
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
We define two types of entropy-based metrics, i. e. (1) the discarding of pixel-wise information used in the forward propagation, and (2) the uncertainty of the input reconstruction, to measure input information contained by a specific layer from two perspectives.
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction
Reducing communication in training large-scale machine learning applications on distributed platform is still a big challenge.
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
A dense block was introduced to improve the performance of extractor in DRU.
Color Image Compression Artifact Reduction
Image Compression Artifact Reduction
+3
Trajectory-User Linking via Variational AutoEncoder
Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification.
Nonparametric Estimation of Low Rank Matrix Valued Function
We also propose another new estimator based on bias-reducing kernels to study the case when $A$ is not necessarily low rank and establish an upper bound on its risk measured by $L_{\infty}$-norm.
On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization
We establish the convergence results of K-AVG for nonconvex objectives and explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is a special case of K-AVG with $K=1$.
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
In addition, the bounds established for HOSVD also elaborate the one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced (or fat) matrices.
