16 papers with code • 13 benchmarks • 10 datasets
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Most implemented papers
Deep Residual Learning for Image Recognition
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
Analyzing Learned Molecular Representations for Property Prediction
In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
How should representations from complementary sensors be integrated for autonomous driving?
Visual Spatial Reasoning
Spatial relations are a basic part of human cognition.
MTet: Multi-domain Translation for English and Vietnamese
We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.