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Found 29 papers, 19 papers with code
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
Knowledge Editing on Black-box Large Language Models
Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge without negatively influencing other knowledge.
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
The proposed dataset contains five types of human-annotated noise, and all those noises are exactly existed in real extensive robust-training methods of slot filling into the proposed framework.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Therefore, in this work, we study a novel problem of developing robust and membership privacy-preserving GNNs.
SCOTT: Self-Consistent Chain-of-Thought Distillation
While CoT can yield dramatically improved performance, such gains are only observed for sufficiently large LMs.
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms.
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization
EEG source localization is an important technical issue in EEG analysis.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Grouping has been commonly used in deep metric learning for computing diverse features.
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks.
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
In our task, saliency maps are used to assist the identification and visualization of developmental landmarks.
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
We evaluate CorDEL with extensive experiments conducted on both public benchmark datasets and a real-world dataset.
Ranked #7 on
Entity Resolution
on Amazon-Google
Global Voxel Transformer Networks for Augmented Microscopy
In this work, we introduce global voxel transformer networks (GVTNets), an advanced deep learning tool for augmented microscopy that overcomes intrinsic limitations of the current U-Net based models and achieves improved performance.
Second-Order Pooling for Graph Neural Networks
In addition, compared to existing graph pooling methods, second-order pooling is able to use information from all nodes and collect second-order statistics, making it more powerful.
Kronecker Attention Networks
Use of attention operators on high-order data requires flattening of the spatial or spatial-temporal dimensions into a vector, which is assumed to follow a multivariate normal distribution.
Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs.
iCapsNets: Towards Interpretable Capsule Networks for Text Classification
On the other hand, iCapsNets explore a novel way to explain the model's general behavior, achieving global interpretability.
Global Pixel Transformers for Virtual Staining of Microscopy Images
It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.
Non-local U-Net for Biomedical Image Segmentation
In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy.
Smoothed Dilated Convolutions for Improved Dense Prediction
Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself.
Large-Scale Learnable Graph Convolutional Networks
However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations.
Ranked #2 on
Document Classification
on Cora
Learning Convolutional Text Representations for Visual Question Answering
We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods.
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information.
Pixel Deconvolutional Networks
When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.
