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Found 13 papers, 3 papers with code
A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
The exponentially enlarged scheduling space and ever-changing optimal parallelism plan from adaptive parallelism together result in the contradiction between low-overhead and accurate performance data acquisition for efficient cluster scheduling.
Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss
The deviation of a classification model is caused by both class-wise and attribute-wise imbalance.
Group Equivariant BEV for 3D Object Detection
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data.
Pre-training Language Models with Deterministic Factual Knowledge
Further experiments on question-answering datasets show that trying to learn a deterministic relationship with the proposed methods can also help other knowledge-intensive tasks.
Semi-supervised Learning with Deterministic Labeling and Large Margin Projection
Optimal leading forest (OLF) has been observed to have the advantage of revealing the difference evolution along a path within a subtree.
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis
We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.
MultiHead MultiModal Deep Interest Recommendation Network
How to obtain the information that users are interested in from the large amount of information has become an issue of great concern to users and even business managers.
Integrating Regular Expressions with Neural Networks via DFA
Human-designed rules are widely used to build industry applications.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions
In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.
Ranked #6 on
Question Answering
on HotpotQA
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
Machine Reading Comprehension (MRC) is a challenging Natural Language Processing(NLP) research field with wide real-world applications.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84. 5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules.
Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data.
