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Found 28 papers, 1 papers with code
Sensing-assisted Robust SWIPT for Mobile Energy Harvesting Receivers
Simultaneous wireless information and power transfer (SWIPT) has been proposed to offer communication services and transfer power to the energy harvesting receiver (EHR) concurrently.
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting
These issues lead the existing methods to a loss of predictive diversity and adherence to the scene constraints.
Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review
This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs.
Statistical inference for pairwise comparison models
Pairwise comparison models have been widely used for utility evaluation and ranking across various fields.
Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16. 9\% accuracy improvement, while being more than 60x smaller than conventional LLMs.
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
This paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility.
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication
Different from conventional radar, the cellular network in the integrated sensing and communication (ISAC) system enables collaborative sensing by multiple sensing nodes, e. g., base stations (BSs).
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i. e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations.
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning
Lastly, our sensitive analysis emphasizes the capability of the proposed framework to handle the long-tailed problem and mitigate the negative impact of noisy labels.
Wasserstein Archetypal Analysis
We prove a consistency result for the regularized problem, ensuring that if the data are iid samples from a probability measure, then as the number of samples is increased, a subsequence of the archetype points converges to the archetype points for the limiting data distribution, almost surely.
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
However, the performance of multi-domain PINNs is sensitive to the choice of the interface conditions.
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand.
Nonparametric Embeddings of Sparse High-Order Interaction Events
High-order interaction events are common in real-world applications.
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition.
Probabilistic methods for approximate archetypal analysis
Archetypal analysis is an unsupervised learning method for exploratory data analysis.
A bandit-learning approach to multifidelity approximation
Multifidelity approximation is an important technique in scientific computation and simulation.
A Global to Local Double Embedding Method for Multi-person Pose Estimation
Both of the two types of methods involve two stages, namely, person detection and joints detection.
Randomized weakly admissible meshes
A weakly admissible mesh (WAM) on a continuum real-valued domain is a sequence of discrete grids such that the discrete maximum norm of polynomials on the grid is comparable to the supremum norm of polynomials on the domain.
Numerical Analysis Numerical Analysis Probability Computation
Open Set Domain Adaptation by Extreme Value Theory
In this paper, we tackle the open set domain adaptation problem under the assumption that the source and the target label spaces only partially overlap, and the task becomes when the unknown classes exist, how to detect the target unknown classes and avoid aligning them with the source domain.
Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels
Extensive experiments on structured and unstructured data for different type of data changes establish that our method consistently outperforms the state-of-the-art methods by a large margin.
Consistency of archetypal analysis
Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data.
Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing
We first propose a novel criterion that guarantees that an $s$-sparse signal is the local minimizer of the $\ell_1/\ell_2$ objective; our criterion is interpretable and useful in practice.
A General Pairwise Comparison Model for Extremely Sparse Networks
Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity.
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
A straightforward solution is to fine-tune a pre-trained source model by using those limited labeled target data, but it usually cannot work well due to the considerable difference between the data distributions of the source and target domains.
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data.
k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers.
Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples.
The Effective Field Theory of Dark Matter Direct Detection
We extend and explore the general non-relativistic effective theory of dark matter (DM) direct detection.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics
