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Found 11 papers, 3 papers with code
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer
Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level.
Type-based Neural Link Prediction Adapter for Complex Query Answering
Answering complex logical queries on incomplete knowledge graphs (KGs) is a fundamental and challenging task in multi-hop reasoning.
RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine
Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment.
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
An effective representation of drug molecules emerges as a pivotal component in this pursuit.
MIM-GAN-based Anomaly Detection for Multivariate Time Series Data
To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN.
CacheGen: KV Cache Compression and Streaming for Fast Language Model Serving
Compared to the recent systems that reuse the KV cache, CacheGen reduces the KV cache size by 3. 7-4. 3x and the total delay in fetching and processing contexts by 2. 7-3. 2x while having negligible impact on the LLM response quality in accuracy or perplexity.
Automatic and Efficient Customization of Neural Networks for ML Applications
ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API.
LA3: Efficient Label-Aware AutoAugment
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training.
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels
The key idea is node classification can benefit from various variants of the original graph that are more efficient for message propagation, based upon the assumption that each variant is a potential structure as more nodes are properly labeled.
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over time.
ALERT: Accurate Learning for Energy and Timeliness
An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans.
