Search Results for author:
Found 42 papers, 16 papers with code
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses.
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
A surrogate function models uncertainty from the limited information.
Zero-shot Generative Linguistic Steganography
Generative linguistic steganography attempts to hide secret messages into covertext.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
Nonlinear energy harvesting system with multiple stability
Secondly, the nonlinear restoring force, friction force, and potential energy surfaces for static characteristics of the energy harvesting system are obtained to show the nonlinear varying stiffness, multiple equilibrium points, discontinuous behaviors and multiple well response.
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey
However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally.
CSG: Curriculum Representation Learning for Signed Graph
Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.
Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models.
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.
XFlow: Benchmarking Flow Behaviors over Graphs
One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios.
Detector Guidance for Multi-Object Text-to-Image Generation
Previous works identify the problem of information mixing in the CLIP text encoder and introduce the T5 text encoder or incorporate strong prior knowledge to assist with the alignment.
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings.
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
AutoMLP: Automated MLP for Sequential Recommendations
Sequential recommender systems aim to predict users' next interested item given their historical interactions.
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks.
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories
In this paper, we propose a novel and flexible conditional diffusion model by introducing conditions into the forward process.
Dual Personalization on Federated Recommendation
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
These imply that the gap corresponds to the lost information of the image, and we can reconstruct the image by filling the gap.
Explainable Information Retrieval: A Survey
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems.
Towards Explainability in NLP: Analyzing and Calculating Word Saliency through Word Properties
These properties may have certain relationships with the word saliency, which is of great help for studying the explainability of the model predictions.
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
We introduce SparcAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks.
ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models
Score-based generative models involve sequentially corrupting the data distribution with noise and then learns to recover the data distribution based on score matching.
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
It is therefore important to conduct user studies to correct models' inference biases and improve the model in a life-long learning manner in the future according to the user feedback.
Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis
Therefore, we propose a Position-based Contributive Embeddings (PosCE) to highlight the historical reference to special position aspect.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization
Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm.
Learnt Sparsification for Interpretable Graph Neural Networks
However, GNNs remain hard to interpret as the interplay between node features and graph structure is only implicitly learned.
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles.
Dissonance Between Human and Machine Understanding
Are humans consistently better at selecting features that make image recognition more accurate?
Explain and Predict, and then Predict Again
A desirable property of learning systems is to be both effective and interpretable.
The sum of the Betti numbers of smooth Hilbert schemes
In this paper, we compute the sum of the Betti numbers for 6 of the 7 families of smooth Hilbert schemes over projective space.
Algebraic Geometry 14C05 (Primary), 14F25 (Secondary)
A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network
Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity.
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary.
REM: From Structural Entropy to Community Structure Deception
This paper focuses on the privacy risks of disclosing the community structure in an online social network.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Contextual Local Explanation for Black Box Classifiers
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.
Asynchronous Training of Word Embeddings for Large Text Corpora
In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings.
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector.
