ICLR 2025 Past AI for science
ICLR 2025 Workshop: XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knowledge
ICLR 2025 Workshop XAI4Science
- Submission deadline
- Feb 11, 2025, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (40)
Fetched from OpenReview (v2) on 2026-06-10.
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$\text{CO}_2$-Net: A Physics-Informed Spatio-Temporal Model for Global $\text{CO}_2$ Reconstruction
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AlphaGo or beta-hCG: a reinforcement learning framework for assisted conception
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Automated Capability Discovery via Model Self-Exploration
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BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts
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Bayesian Concept Bottleneck Models with LLM Priors
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Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
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Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences
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Causally Reliable Concept Bottleneck Models
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Circuit mechanism for compositional induction in transformer
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Counterfactual Concept Bottleneck Models
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Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation
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Emergence of Computational Structure in a Neural Network Physics Simulator
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From Markov to Laplace: How Mamba In-Context Learns Markov Chains
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Generating $\pi$-Functional Molecules Using STGG+ with Active Learning
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Graph Discrete Diffusion: a Spectral Study
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Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
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LEARNING MULTIPHASE AND MULTIPHYSICS SYSTEM WITH DECOUPLED STATE SPACE MODEL
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LENS: Learning and Evolving Numerical Scores for Cohort-Specific Clinical Insights
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Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory
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Machine learning-based Optimization for Molten pool Dynamics in Laser Manufacturing
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Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability
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Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
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Modeling Multi-Regional and Non-Stationary Neural Dynamics via Latent Sub-Circuits
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Moment Neural Operator: Interpretable mapping in discontinuous function spaces
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NeuralDEM: Real-time Simulation of Industrial Particulate Flows
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Piecewise Polynomial Regression of Tame Functions via Integer Programming
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Post-hoc Interpretability Illumination for Scientific Interaction Discovery
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Reconstructing Dynamics from Steady Spatial Patterns with Partial Observations
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Rethinking Visual Counterfactual Explanations Through Region Constraint
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SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
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Scaling Sparse Autoencoders for Interpreting Protein Structure Prediction
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SHAP-BASED A-POSTERIORI INTERPRETABILITY FOR GRAPH NEURAL NETWORKS IN CFD-BASED SUSTAINABLE BUILDING SIMULATIONS
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Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data
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Spatially-Informed Sampling Enables Accurate Prediction of Large-Scale Mutational Effects
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TIME-AWARE FEATURE SELECTION: ADAPTIVE TEMPORAL MASKING FOR STABLE SPARSE AUTOENCODER TRAINING
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TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
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Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs
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ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding
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Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis
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Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations