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Found 14 papers, 8 papers with code
Large Language Model Empowered Embedding Generator for Sequential Recommendation
RAT refines the embeddings to be optimally suited for SRS.
Bidirectional Gated Mamba for Sequential Recommendation
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences.
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to large computational burden.
AFter: Attention-based Fusion Router for RGBT Tracking
In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure.
Ranked #4 on
Rgb-T Tracking
on RGBT210
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
MLPST: MLP is All You Need for Spatio-Temporal Prediction
To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
AutoDenoise: Automatic Data Instance Denoising for Recommendations
In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.
AutoMLP: Automated MLP for Sequential Recommendations
Sequential recommender systems aim to predict users' next interested item given their historical interactions.
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
