haebom
Daily Arxiv
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SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders
Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation
DeepEyesV2: Toward Agentic Multimodal Model
MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages
Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs
multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder
Addressing divergent representations from causal interventions on neural networks
Alternative Fairness and Accuracy Optimization in Criminal Justice
Leveraging LLM-based agents for social science research: insights from citation network simulations
Conversational Collective Intelligence (CCI) using Hyperchat AI in a Real-world Forecasting Task
Explaining Human Choice Probabilities with Simple Vector Representations
Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control
GraphCliff: Short-Long Range Gating for Subtle Differences but Critical Changes
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation
Reasoning Planning for Language Models
Aligning Brain Signals with Multimodal Speech and Vision Embeddings
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models
Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets
Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs
Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models
Meronymic Ontology Extraction via Large Language Models
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops
The Markovian Thinker
scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks
TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
Pure Vision Language Action (VLA) Models: A Comprehensive Survey
UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning
Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence
Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction
UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning
Normality and the Turing Test
GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs
Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach
ANO : Faster is Better in Noisy Landscape
Generative Medical Event Models Improve with Scale
ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data
Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment
Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning
Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor
Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection
Controllable Hybrid Captioner for Improved Long-form Video Understanding
Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework
Evaluating LLM-based Workflows for Switched-Mode Power Supply Design
Monitoring Risks in Test-Time Adaptation
White-Basilisk: A Hybrid Model for Code Vulnerability Detection
Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery
CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks
DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
GPT, But Backwards: Exactly Inverting Language Model Outputs
Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs
ReCode: Updating Code API Knowledge with Reinforcement Learning
Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting
Sekai: A Video Dataset towards World Exploration
Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation
A Fairness-Aware Strategy for B5G Physical-layer Security Leveraging Reconfigurable Intelligent Surfaces
Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation
Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies
GRAM: Spatial general-purpose audio representation models for real-world applications
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video
When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering
Rethinking Text-based Protein Understanding: Retrieval or LLM?
Language Model Distillation: A Temporal Difference Imitation Learning Perspective
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis
Guided Diffusion Sampling on Function Spaces with Applications to PDEs
The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute
DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations
When Bias Helps Learning: Bridging Initial Prejudice and Trainability
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real
Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering
Video CLIP Model for Multi-View Echocardiography Interpretation
Quantum Doubly Stochastic Transformers
ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness
Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models
Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric
Simulator Ensembles for Trustworthy Autonomous Driving Testing
Continual Pre-training of MoEs: How robust is your router?
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IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
Created by
Haebom
저자
Beiliang Wu, Peiyuan Liu, Yifan Hu, Luyan Zhang, Ao Hu, Zenglin Xu
개요
본 논문은 시계열 예측에서 인덱스 관련 정보를 활용하는 MLP 기반 프레임워크인 IndexNet을 제안한다. IndexNet은 시간 정보를 임베딩하는 Timestamp Embedding (TE)과 변수 인덱스를 임베딩하는 Channel Embedding (CE) 모듈을 포함하여 장기적인 주기적 패턴을 포착하고 다양한 변수를 구별한다. 12개의 실제 데이터셋을 사용한 실험을 통해 IndexNet의 성능과 해석 가능성을 입증했다.
시사점, 한계점
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시사점:
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시간 정보와 변수 인덱스 정보를 활용하여 시계열 예측 모델의 성능을 향상시킴
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MLP 기반 아키텍처를 사용하여 계산 효율성을 유지하면서 강력한 성능을 달성
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Plug-and-play 실험을 통해 IndexNet의 일반성을 입증
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시계열 예측 연구에서 중요하게 다루어지지 않았던 해석 가능성을 제시
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한계점:
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구체적인 한계점은 논문 초록에 명시되어 있지 않음.
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