Search Results for author:
Found 13 papers, 6 papers with code
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline.
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN).
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
The efficacy of training larger models is evaluated using realistic hardware parameters and shows that that analog CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software trained models.
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput.
Hand Gestures Recognition in Videos Taken with Lensless Camera
It is created specifically for raw video captured by a lensless camera and has the ability to properly extract and combine temporal and spatial features.
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e. g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras.
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph.
Ranked #19 on
Relation Extraction
on DocRED
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
Causal inference is the process of capturing cause-effect relationship among variables.
Neighborhood Matching Network for Entity Alignment
This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.
Jointly Learning Entity and Relation Representations for Entity Alignment
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Ranked #18 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #20 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Remaining useful life estimation of engineered systems using vanilla LSTM neural networks
Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises.
Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules.
