Search Results for author:
Found 32 papers, 8 papers with code
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation
In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC.
Ranked #22 on
Emotion Recognition in Conversation
on EmoryNLP
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots
In this work, we investigate the problem of satisfaction states tracking and its effects on CSP in E-commerce service chatbots.
CodeContests+: High-Quality Test Case Generation for Competitive Programming
Competitive programming, due to its high reasoning difficulty and precise correctness feedback, has become a key task for both training and evaluating the reasoning capabilities of large language models (LLMs).
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks.
Open set label noise learning with robust sample selection and margin-guided module
The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space.
Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On
Image-based virtual try-on is challenging since the generated image should fit the garment to model images in various poses and keep the characteristics and details of the garment simultaneously.
Sequential Network Design
When the social planner is sufficiently myopic, it is optimal to form a quasi-complete graph at each period, which is unique up to permutation.
Are Existing Road Design Guidelines Suitable for Autonomous Vehicles?
In this work, we introduce a black box attack on the perception systems of AVs, in which the objective is to create realistic adversarial scenarios (i. e. satisfying road design guidelines) by manipulating the positions of common roadside objects, and without resorting to `unnatural' adversarial patches.
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation
DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup.
Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets.
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
The further experimental results in downstream tasks, 2D image segmentation and 3D image registration, indicate significant improvements resulting from DRDM, showcasing the potential of our model to advance image manipulation and synthesis in medical imaging and beyond.
FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network
In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation.
Task-Customized Mixture of Adapters for General Image Fusion
These adapters are shared across different tasks and constrained by mutual information regularization, ensuring compatibility with different tasks while complementarity for multi-source images.
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.
Learning with Noisy Labels for Human Fall Events Classification: Joint Cooperative Training with Trinity Networks
The robustness and performance of the noisy label learning framework is improved by using the two teacher modules and one student module in the proposed JoCoT.
MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo
We design a multi-scale windows PatchMatch (mPM) to obtain reliable depth of untextured areas.
Multi-Task Cooperative Learning via Searching for Flat Minima
In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach.
Maximizing the performance for microcomb based microwave photonic transversal signal processors
Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photonic processing.
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system.
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge
In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively.
Two-stage Fall Events Classification with Human Skeleton Data
For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification.
Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks
The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products.
Enhancing Top-N Item Recommendations by Peer Collaboration
In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection.
$FM^2$: Field-matrixed Factorization Machines for Recommender Systems
The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.
Urca Cooling in Neutron Star Crusts and Oceans: Effects of Nuclear Excitations
The excited-state structure of atomic nuclei can modify nuclear processes in stellar environments.
Nuclear Theory High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
Structural Interventions in Networks
Two types of interventions are commonly implemented in networks: characteristic intervention, which influences individuals' intrinsic incentives, and structural intervention, which targets the social links among individuals.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.
MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images
In this paper, we propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images.
A Generic Network Compression Framework for Sequential Recommender Systems
Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer.
IS THE LABEL TRUSTFUL: TRAINING BETTER DEEP LEARNING MODEL VIA UNCERTAINTY MINING NET
During the training stage, we utilize all the available data (labeled and unlabeled) to train the classifier via a semi-supervised generative framework.
Overcoming the time limitation in Molecular Dynamics simulation of crystal nucleation: a persistent-embryo approach
The crystal nucleation from liquid in most cases is too rare to be accessed within the limited timescales of the conventional molecular dynamics (MD) simulation.
Materials Science Soft Condensed Matter
