Search Results for author:
Found 22 papers, 11 papers with code
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications.
NLQxform-UI: A Natural Language Interface for Querying DBLP Interactively
In recent years, the DBLP computer science bibliography has been prominently used for searching scholarly information, such as publications, scholars, and venues.
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
To incorporate sufficient diversity into the IoT training data, one therefore needs to consider a combinatorial explosion of training cases that are multiplicative in the number of objects considered and the possible environmental conditions in which such objects may be encountered.
GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Most current methods for multi-hop question answering (QA) over knowledge graphs (KGs) only provide final conclusive answers without explanations, such as a set of KG entities that is difficult for normal users to review and comprehend.
NLQxform: A Language Model-based Question to SPARQL Transformer
In recent years, scholarly data has grown dramatically in terms of both scale and complexity.
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics.
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones.
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Second, the potentially dynamic distributions from the initially observable facts to the future facts ask for explicitly modeling the evolving characteristics of new entities.
QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs
Multi-relation question answering (QA) is a challenging task, where given questions usually require long reasoning chains in KGs that consist of multiple relations.
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
Limits of PageRank-based ranking methods in sports data
We propose a new PageRank variant which outperforms PageRank in all evaluated settings, yet shares its sensitivity to increased randomness in the data.
Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US
We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i. e., in lieu of social distancing).
Physics and Society Computers and Society Social and Information Networks
Revisiting Over-smoothing in Deep GCNs
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs).
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
In the synthetic dataset, our model reduces error by 40%.
Joint Embedding Learning of Educational Knowledge Graphs
However, in educational knowledge graphs, structural relationships are not the focus.
Structured Query Construction via Knowledge Graph Embedding
At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
