https://github.com/ElementAI/beyond-trivial-explanations
공식 코드Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness Reasoning
FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
Decision Information Meets Large Language Models: The Future of Explainable Operations Research
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
Unsupervised and self-adaptative techniques for cross-domain person re-identification
A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation
An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks
RiskBench: A Scenario-based Benchmark for Risk Identification
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems
How Should We Represent History in Interpretable Models of Clinical Policies?
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
Modeling and Leveraging Prerequisite Context in Recommendation
Reinforcement Learning Pair Trading: A Dynamic Scaling approach
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
DERAIL: Diagnostic Environments for Reward And Imitation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.