Search Results for author:
Found 21 papers, 10 papers with code
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.
MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities
The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class.
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation
Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making.
ViCE: Visual Counterfactual Explanations for Machine Learning Models
The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application.
iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
Error analysis in NLP models is essential to successful model development and deployment.
Benchmark Time Series Database with IoTDB-Benchmark for IoT Scenarios
With the wide application of time series databases (TSDB) in big data fields like cluster monitoring and industrial IoT, there have been developed a number of TSDBs for time series data management.
Databases
Mind the Pad -- CNNs can Develop Blind Spots
We show how feature maps in convolutional networks are susceptible to spatial bias.
Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition
This scheme leverages motion cues such as motion boundary and motion magnitude (in contrast, camera motion is usually considered as "noise" for most previous methods) to generate a more compact and discriminative set of object proposals, which are more closely related to the objects which are being manipulated.
Half-CNN: A General Framework for Whole-Image Regression
This is a generic regression framework that fits many applications.
Temporal Action Localization With Pyramid of Score Distribution Features
We investigate the feature design and classification architectures in temporal action localization.
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem.
Chirality-enabled optical dipole potential energy for two-level atoms
The beam is characterized by the existence of a longitudinal electric field component which is responsible for the appearance of a chiral term in the optical dipole potential energy.
Optics Atomic Physics
Visualizing Rule Sets: Exploration and Validation of a Design Space
Rule sets are typically presented as a text-based list of logical statements (rules).
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets
Rule sets are typically presented as a text-based list of logical statements (rules).
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations.
Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles
To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles.
JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval
In this paper, we propose a highly accurate method for joint estimation of pitch, onset and offset, named JEPOO.
Equitable Multi-task Learning
To solve the issue, we in-depth investigate the equity problem for MTL and find that regularizing relative contribution of different tasks (i. e. value of task-specific loss divides its raw gradient norm) in updating shared parameter can improve generalization performance of MTL.
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers
In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods.
