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Found 21 papers, 9 papers with code
Maximal Clique Based Non-Autoregressive Open Information Extraction
However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.
DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning
It has long been assumed that the sheer number of parameters in large language models (LLMs) drives in-context learning (ICL) capabilities, enabling remarkable performance improvements by leveraging task-specific demonstrations.
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data.
On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations
Namely, we show that when a small number of cells (e. g. 1%-5% of cells) have their positions shifted such that a measure of global congestion is guaranteed to remain unaffected (e. g. 1% of the design adversarially shifted by 0. 001% of the layout space results in a predicted decrease in congestion of up to 90%, while no change in congestion is implied by the perturbation).
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.
Graph-Aware Contrasting for Multivariate Time-Series Classification
As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Assessment of Reinforcement Learning for Macro Placement
We provide open, transparent implementation and assessment of Google Brain's deep reinforcement learning approach to macro placement and its Circuit Training (CT) implementation in GitHub.
Intellectual Property Evaluation Utilizing Machine Learning
Intellectual properties is increasingly important in the economic development.
Document-Level Event Extraction via Human-Like Reading Process
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
GenReg: Deep Generative Method for Fast Point Cloud Registration
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
Distilling Holistic Knowledge with Graph Neural Networks
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous.
Duality between two generalized Aubry-Andre models with exact mobility edges
A mobility edge (ME) in energy separating extended from localized states is a central concept in understanding various fundamental phenomena like the metal-insulator transition in disordered systems.
Disordered Systems and Neural Networks
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.
Ranked #2 on
Relation Extraction
on NYT11-HRL
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels.
Edge corona product as an approach to modeling complex simplical networks
Many graph products have been applied to generate complex networks with striking properties observed in real-world systems.
Discrete Mathematics Social and Information Networks
Deep Bi-Dense Networks for Image Super-Resolution
In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Automatic essay scoring (AES) is the task of assigning grades to essays without human interference.
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals.
