Imagen de Portada

TDHook: A Lightweight Framework for Interpretability

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Poupart, Yoann
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents
por: Poupart, Yoann
Publicado: (2024)

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning
por: Poupart, Yoann, et al.
Publicado: (2025)

Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
por: Boget, Yoann
Publicado: (2025)

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models
por: Miao, Yanting, et al.
Publicado: (2025)

LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis
por: Wei, Zhihuan, et al.
Publicado: (2026)

Why Online Reinforcement Learning is Causal
por: Schulte, Oliver, et al.
Publicado: (2024)

Measures of Variability for Risk-averse Policy Gradient
por: Luo, Yudong, et al.
Publicado: (2025)

Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow
por: Delestre, Cyrile, et al.
Publicado: (2024)

Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
por: Jeong, Jihwan, et al.
Publicado: (2025)

A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
por: Liu, Guiliang, et al.
Publicado: (2024)

LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems
por: Yu, Fengyuan, et al.
Publicado: (2025)

Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs
por: Pepper, Keenan, et al.
Publicado: (2026)

TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
por: Xia, Mingyuan, et al.
Publicado: (2025)

RL-Struct: A Lightweight Reinforcement Learning Framework for Reliable Structured Output in LLMs
por: Hu, Ruike, et al.
Publicado: (2025)

A Lightweight Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes
por: Liu, Meiliang, et al.
Publicado: (2025)

The Reciprocity Gradient
por: Lin, Yue, et al.
Publicado: (2026)

Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation
por: Zhao, Jitian, et al.
Publicado: (2025)

Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
por: Qi, Ji, et al.
Publicado: (2025)

An Interpretable and Scalable Framework for Evaluating Large Language Models
por: Qu, Xinhao, et al.
Publicado: (2026)

EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
por: Zhang, Shu-Hao, et al.
Publicado: (2026)

A Lightweight LLM Framework for Disaster Humanitarian Information Classification
por: Jinzhen, Han, et al.
Publicado: (2026)

REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
por: Li, Bo, et al.
Publicado: (2025)

A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting
por: He, Cheng, et al.
Publicado: (2025)

CANDLE: A Cross-Modal Agentic Knowledge Distillation Framework for Interpretable Sarcopenia Diagnosis
por: Jin, Yuqi, et al.
Publicado: (2025)

Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
por: Lukyanov, Kirill, et al.
Publicado: (2025)

A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
por: Fatemi, Pouria, et al.
Publicado: (2025)

Interpretable Discovery of One-parameter Subgroups: A Modular Framework for Elliptical, Hyperbolic, and Parabolic Symmetries
por: Karjol, Pavan, et al.
Publicado: (2025)

CoPE: A Lightweight Complex Positional Encoding
por: Amballa, Avinash
Publicado: (2025)

Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
por: Veljković, Tin Hadži, et al.
Publicado: (2026)

Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
por: Yang, Zixuan, et al.
Publicado: (2024)

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
por: Zhang, Fuyao, et al.
Publicado: (2025)

FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
por: Balija, Sree Bhargavi
Publicado: (2025)

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
por: Huang, Rui, et al.
Publicado: (2026)

Lightweight Cross-Modal Representation Learning
por: Faye, Bilal, et al.
Publicado: (2024)

Lightweight Edge Learning via Dataset Pruning
por: Ale, Laha, et al.
Publicado: (2026)

An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
por: Chen, Jingke, et al.
Publicado: (2026)

A Theory of Interpretable Approximations
por: Bressan, Marco, et al.
Publicado: (2024)

A Lightweight Multi-View Approach to Short-Term Load Forecasting
por: Guité-Vinet, Julien, et al.
Publicado: (2026)

A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
por: Liu, Jingyi, et al.
Publicado: (2026)

A General Framework for Producing Interpretable Semantic Text Embeddings
por: Sun, Yiqun, et al.
Publicado: (2024)