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Found 15 papers, 5 papers with code
Preference Discerning with LLM-Enhanced Generative Retrieval
We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences.
Async Learned User Embeddings for Ads Delivery Optimization
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance.
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data.
Chinese Spelling Correction as Rephrasing Language Model
However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error.
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD.
Kriging Convolutional Networks
Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends.
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
In this work, we convert graph pruning to a problem of node relabeling and then relax it to a differentiable problem.
Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data
The model uses a novel Cascaded Modality Transformer architecture with cross-attention to incorporate multi-modal information for more informed predictions.
Structure Guided Manifolds for Discovery of Disease Characteristics
DiDiGAN learns a disease manifold of AD and CN visual characteristics, and the style codes sampled from this manifold are imposed onto an anatomical structural "blueprint" to synthesise paired AD and CN magnetic resonance images (MRIs).
Stochastic Iterative Graph Matching
In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.
Modeling Graph Node Correlations with Neighbor Mixture Models
We propose a new model, the Neighbor Mixture Model (NMM), for modeling node labels in a graph.
Universal Representation for Code
Learning from source code usually requires a large amount of labeled data.
Localizing and Amortizing: Efficient Inference for Gaussian Processes
Additionally, the inference task is decomposed to small subtasks with several technique innovations, making our model well suits the stochastic optimization.
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes
Many recent inference methods approximate the posterior distribution with a simpler distribution defined on a small number of inducing points.
