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Found 33 papers, 9 papers with code
Continual Few-shot Intent Detection
Intent detection is at the core of task-oriented dialogue systems.
Incorporating Casual Analysis into Diversified and Logical Response Generation
Although the Conditional Variational Auto-Encoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning
Large Language Models (LLMs) have shown significant challenges in detecting and repairing vulnerable code, particularly when dealing with vulnerabilities involving multiple aspects, such as variables, code flows, and code structures.
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
In this paper, we introduce SocialBench, the first benchmark designed to systematically evaluate the sociality of role-playing conversational agents at both individual and group levels of social interactions.
From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News
Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations.
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering.
Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator
To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models
In this paper, we propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents, including vehicles, bicycles, pedestrians, etc., in a scene.
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences.
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations
In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts.
ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval
The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream.
Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks
Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving.
Ranked #1 on
Trajectory Prediction
on Argoverse2
Incorporating Causal Analysis into Diversified and Logical Response Generation
Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
A multi-aspect attentive network is proposed to automatically attend to different aspects in a review and ensure most of the issues are tackled.
A Unified Analysis of Dynamic Interactive Learning
In this paper we investigate the problem of learning evolving concepts over a combinatorial structure.
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification.
Graph Convolutional Networks via Adaptive Filter Banks
Graph convolutional networks have been a powerful tool in representation learning of networked data.
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.
Photometric and Spectroscopic Study of Flares on Ross 15
We conducted photometric and spectroscopic observations for Ross 15 in order to further study the flare properties of this less observed flare star.
Solar and Stellar Astrophysics High Energy Astrophysical Phenomena
Graph Pooling with Node Proximity for Hierarchical Representation Learning
In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices
We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer.
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting.
Online Bagging for Anytime Transfer Learning
In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
Simple and Effective Text Matching with Richer Alignment Features
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Ranked #5 on
Natural Language Inference
on SciTail
iPool -- Information-based Pooling in Hierarchical Graph Neural Networks
In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs.
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
