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Found 32 papers, 13 papers with code
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding
Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i. e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances.
T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration
This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors.
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering
To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner.
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations.
Transport-Hub-Aware Spatial-Temporal Adaptive Graph Transformer for Traffic Flow Prediction
Finally, we design an extra spatial-temporal knowledge distillation module for incremental learning of traffic flow prediction tasks.
Attention-Based Acoustic Feature Fusion Network for Depression Detection
To rectify this, we present the novel Attention-Based Acoustic Feature Fusion Network (ABAFnet) for depression detection.
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e. g., intents and slots).
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion.
Variable Clustering via Distributionally Robust Nodewise Regression
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression.
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography.
BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning
Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years.
Fast density peaks clustering algorithm in polar coordinate system
Density peaks clustering (DPC) algorithm provides an efficient method to quickly find cluster centers with decision graphs.
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e. g., intent and slots).
Ranked #1 on
Semantic Frame Parsing
on ProSLU
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
Ranked #1 on
Semantic Frame Parsing
on MixATIS
(Overall Accuracy metric)
Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study
LT set contained patients with HCC treated by LT.
Asset Selection via Correlation Blockmodel Clustering
Finally, we conduct an empirical analysis to verify the performance of the algorithm.
A machine-learning framework for peridynamic material models with physical constraints
We generate macro-scale deformation training data by averaging over periodic micro-structure unit cells and add a physical energy constraint representing the homogenized elastic modulus of the micro-structure to the regression algorithm.
Numerical Analysis Numerical Analysis Applied Physics
The optimal investment strategy of a DC pension plan under deposit loan spread and the O-U process
This paper is devoted to invest an optimal investment strategy for a defined-contribution (DC) pension plan under the Ornstein-Uhlenbeck (O-U) process and the loan.
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains.
Ranked #1 on
Task-Oriented Dialogue Systems
on Kvret
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.
Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests
A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users.
Memory-Constrained No-Regret Learning in Adversarial Bandits
An adversarial bandit problem with memory constraints is studied where only the statistics of a subset of arms can be stored.
Distributed No-Regret Learning in Multi-Agent Systems
In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games.
Inpatient2Vec: Medical Representation Learning for Inpatients
In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning.
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs
Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.
