Paper Library - AiPaper

GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation

Published:6/22/2024

Geometry-Aware Large Reconstruction Model3D Gaussian Generation3D-aware Transformer StructureSparse 3D Structure OptimizationDeformable Cross-Attention Mechanism

This study introduces GeoLRM, a geometryaware large reconstruction model that efficiently generates highquality 3D assets with 512k Gaussians from 21 input images using only 11 GB of GPU memory. It addresses limitations of existing methods by utilizing a novel 3Daware transfor

Wide-FOV 3D Pancake VR Enabled by a Light Field Display Engine

Light Field Display EngineWide-FOV 3D Virtual RealityComputational Focus CuesMicro-LCDOptical Pancake Design

This paper presents a novel true3D Pancake VR system using a light field display engine and computational focus cues, achieving highresolution images. It addresses FOV reduction due to aberrations with a telecentric path, experimentally confirming clear 3D images with a 68.6de

The differential influence of Achievement Motivation on Subjective Well-being and the moderating role of Self-control

Published:9/27/2024

Achievement Motivation and Subjective Well-BeingModerating Role of Self-ControlPsychological Health Studies in College StudentsSelf-Management and Well-Being

This study surveyed 1,017 Chinese college students to explore the relationship between achievement motivation and subjective wellbeing, revealing that selfcontrol significantly moderates this relationship. High selfcontrol enhances positive impacts of success motivation and mi

Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-package

Published:5/6/2024

Impact Analysis of Machine Learning in EducationBibliometrix Statistical Analysis MethodInterdisciplinary Research on Machine Learning and EducationTrends and Patterns in Educational ResearchChallenges and Ethical Considerations in ML Education Applicatio

This study uses bibliometric analysis to explore machine learning's impact on education, revealing its transformative potential for teaching methods. Analyzing 970 articles from 2000 to 2023 identifies growth patterns and key contributors, providing a comprehensive roadmap for in

A multifactorial model of intrinsic / environmental motivators, personal traits and their combined influences on math performance in elementary school

Achievement Goals Model for Math PerformanceInfluence of Self-Efficacy and InterestRole of Environmental Factors in Learning MotivationHolistic Multifactorial Path AnalysisElementary School Math Learning Research

This study develops a comprehensive multifactorial path analysis model to explore the influences of intrinsic and environmental motivators and personality traits on math performance among elementary students. Results from 762 Cypriot students highlight selfefficacy and interest

Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation

Published:9/11/2025

Multimodal Recommendation SystemsFine-Grained Cross-Modal Association ModelingBidirectional Attention MechanismGlobal Distribution Consistency RegularizationDilated Refinement Attention Module

This study presents MambaRec, an innovative multimodal recommendation framework addressing finegrained crossmodal association modeling and global consistency issues through attentionguided learning. Its core contribution is the Dilated Refinement Attention Module, enhancing fu

Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals

Published:10/8/2025

Multimodal Generative Recommendation SystemFusion of Collaborative and Semantic SignalsSelf-Supervised Quantization LearningSequential Recommender SystemsDINO Framework

The paper introduces MSCGRec, a generative recommendation system that addresses limitations in current sequential recommenders by integrating multiple semantic modalities and collaborative features. Empirical results show superior performance on three realworld datasets, validat

MorphQPV: Exploiting Isomorphism in Quantum Programs to Facilitate Confident Verification

Published:4/24/2024

Quantum Program VerificationIsomorphism MethodConfident Assertion-Based Verification MethodConstraint Optimization ProblemQuantum Algorithm Debugging

MorphQPV is a confident assertionbased verification method for quantum programs, leveraging isomorphism to establish structural preservation relations among runtime states. It transforms verification into a constraint optimization problem, significantly improving efficiency and

ModRWKV: Transformer Multimodality in Linear Time

Published:11/1/2025

ModRWKV Multimodal FrameworkRWKV ArchitectureLinear-Time TransformerMultimodal Large Language ModelsDynamically Adaptable Heterogeneous Modality Encoders

This study introduces ModRWKV, a framework based on RWKV architecture that achieves multimodal processing with linear time complexity, outperforming traditional quadraticcomplexity Transformer models. It balances performance and computational efficiency for multisource informat

Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation

Published:9/11/2024

Generative Recommendation SystemsMulti-Aspect Semantic TokenizationText-Based Reconstruction TasksLong-Tailed Recommendation IssuesCold-Start Recommendation

This paper introduces LAMIA, a novel multiaspect semantic tokenization framework that enhances generative recommendation systems. Unlike traditional methods, it learns independent embeddings capturing multiple facets of items, significantly improving recommendation accuracy for

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

Published:12/16/2017

Tacotron 2 Speech SynthesisWaveNet VocoderMel Spectrogram PredictionSequence-to-Sequence Feature PredictionNeural Network Speech Synthesis Architecture

This paper presents Tacotron 2, a neural network architecture for direct texttospeech synthesis. It features a sequencetosequence network predicting mel spectrograms and a modified WaveNet as a vocoder. The model achieves a mean opinion score of 4.53, comparable to profession

Tacotron: Towards End-to-End Speech Synthesis

Published:3/30/2017

End-to-End Speech Synthesis ModelTacotron ModelSequence-to-Sequence LearningText-to-Speech SynthesisGenerative Models in NLP

Tacotron is an endtoend texttospeech model that synthesizes speech directly from characters, simplifying complex traditional TTS systems. Trained from scratch, it scores 3.82 in mean opinion, outperforming existing systems in naturalness and offering faster generation speeds.

WaveNet: A Generative Model for Raw Audio

Published:9/13/2016

Audio Generation ModelWaveNet ArchitectureText-to-Speech SynthesisAutoregressive ModelingMusic Generation

WaveNet is introduced as a deep neural network for raw audio generation, featuring probabilistic and autoregressive properties. It excels in texttospeech tasks, surpassing existing systems in naturalness, and shows high realism in music generation while also achieving promising

End-to-End Speech Recognition Contextualization with Large Language Models

Published:9/20/2023

LLM-based Speech RecognitionText Context Augmented Speech RecognitionMixed-Modal Language ModelingDecoder-Only Speech RecognitionLow-Parameter Adapter Method

The paper introduces a novel speech recognition contextualization method using Large Language Models, reframing ASR as mixedmodal language modeling. Adding textual context reduces word error rate by 6%, achieving a 7.5% improvement over the baseline system.

End-to-End Speech Recognition: A Survey

Published:3/3/2023

End-to-End Speech Recognition ArchitecturesDeep Learning in Speech RecognitionAll-Neural ASR ModelsTaxonomy of Automatic Speech Recognition ModelsTraining and Decoding of Speech Recognition Models

This survey reviews advancements in endtoend automatic speech recognition (ASR) models, highlighting deep learning's impact on reducing word error rates. It provides a taxonomy of E2E models, discusses their properties, relates them to traditional hidden Markov models, and cove

Fun-ASR Technical Report

Published:9/16/2025

LLM-based Automatic Speech Recognition SystemOptimizations for Practical Deployment in Speech RecognitionReinforcement Learning Applications in Speech RecognitionData-Driven Large-Scale Speech RecognitionStreaming Capability in Speech Recognition

FunASR integrates large data, model capacity, and deep LLM integration, optimizing through reinforcement learning for realworld challenges. It achieves stateoftheart performance on industrial datasets, demonstrating effectiveness in streaming, noise robustness, and codeswit

FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

Published:7/5/2024

Multilingual Speech Recognition and GenerationEmotion Recognition in SpeechSpeech-to-Speech TranslationZero-Shot Voice CloningNatural Voice Interaction between Humans and LLMs

This report presents FunAudioLLM, a model family enhancing natural voice interaction between humans and LLMs. It features SenseVoice for multilingual speech and emotion recognition, and CosyVoice for natural voice generation, supporting applications like speech translation and em

A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition

Published:1/1/1990

Hidden Markov ModelsStatistical Modeling in Speech RecognitionApplications of Markov Source ModelsReview of Statistical Methods

This tutorial reviews the theory of Hidden Markov Models (HMMs) and their applications in speech recognition, highlighting their rich mathematical structure and effectiveness in practical applications.

Jenga: Enhancing LLM Long-Context Fine-tuning with Contextual Token Sparsity

Large Language Model Fine-TuningLong-Context ModelingSparse Attention Mechanism

Jenga is a novel LLM finetuning system that optimizes activation memory usage in longcontext applications using Contextual Token Sparsity. It employs token elimination, pattern prediction, and kernel optimization, achieving up to 1.93x memory reduction and 1.36x acceleration ov

Objaverse-XL: A Universe of 10M+ 3D Objects

Published:7/11/2023

Objaverse-XL Dataset3D Vision TasksMulti-View Rendered ImagesZero-Shot Generalization CapabilityAcquisition of High-Quality 3D Data

The paper introduces ObjaverseXL, a dataset of over 10 million 3D objects, addressing the scarcity of highquality data in 3D vision tasks. Training on 100 million multiview images achieved significant zeroshot generalization, promoting innovation in 3D vision.

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