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Found 23 papers, 8 papers with code
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis
SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL).
WESE: Weak Exploration to Strong Exploitation for LLM Agents
Concretely, WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge.
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework.
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs).
Understanding the planning of LLM agents: A survey
As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention.
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
Large Language Models (LLMs) have achieved remarkable results in the machine translation evaluation task, yet there remains a gap in knowledge regarding how they utilize the provided data to conduct evaluations.
Cross-target Stance Detection by Exploiting Target Analytical Perspectives
In this paper, we propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge.
RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability
Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training.
A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems
Recommendation systems effectively guide users in locating their desired information within extensive content repositories.
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system.
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence.
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web services.
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint
Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness.
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering.
A Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images
Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results.
Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images
Individual tree detection and crown delineation (ITDD) are critical in forest inventory management and remote sensing based forest surveys are largely carried out through satellite images.
Geometric Processing for Image-based 3D Object Modeling
Nowadays the image-based methods backboned by the recently developed advanced dense image matching algorithms and geo-referencing paradigms, are becoming the dominant approaches, due to its high flexibility, availability and low cost.
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars' unique features.
Robotics
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
Nowadays dense stereo matching has become one of the dominant tools in 3D reconstruction of urban regions for its low cost and high flexibility in generating dense 3D points.
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics.
Multi-View Large-Scale Bundle Adjustment Method for High-Resolution Satellite Images
Given enough multi-view image corresponding points (also called tie points) and ground control points (GCP), bundle adjustment for high-resolution satellite images is used to refine the orientations or most often used geometric parameters Rational Polynomial Coefficients (RPC) of each satellite image in a unified geodetic framework, which is very critical in many photogrammetry and computer vision applications.
