What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Mohan, Arvind, Chattopadhyay, Ashesh, Miller, Jonah |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
por: Wen, Jiawen, et al.
Publicado: (2024)
por: Wen, Jiawen, et al.
Publicado: (2024)
What You See is What You Classify: Black Box Attributions
por: Stalder, Steven, et al.
Publicado: (2022)
por: Stalder, Steven, et al.
Publicado: (2022)
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
por: Cai, Will, et al.
Publicado: (2025)
por: Cai, Will, et al.
Publicado: (2025)
Generative forecasting with joint probability models
por: Wyrod, Patrick, et al.
Publicado: (2025)
por: Wyrod, Patrick, et al.
Publicado: (2025)
Tell What You Hear From What You See -- Video to Audio Generation Through Text
por: Liu, Xiulong, et al.
Publicado: (2024)
por: Liu, Xiulong, et al.
Publicado: (2024)
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
por: Trevithick, Alex, et al.
Publicado: (2024)
por: Trevithick, Alex, et al.
Publicado: (2024)
Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
por: Liu, Zhaoyang, et al.
Publicado: (2025)
por: Liu, Zhaoyang, et al.
Publicado: (2025)
LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
por: Guan, Haiwen, et al.
Publicado: (2024)
por: Guan, Haiwen, et al.
Publicado: (2024)
LUCIE-3D: A three-dimensional climate emulator for forced responses
por: Guan, Haiwen, et al.
Publicado: (2025)
por: Guan, Haiwen, et al.
Publicado: (2025)
More Compute Is What You Need
por: Guo, Zhen
Publicado: (2024)
por: Guo, Zhen
Publicado: (2024)
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
por: Heimbach, Lothar, et al.
Publicado: (2025)
por: Heimbach, Lothar, et al.
Publicado: (2025)
Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
por: Zhai, Jucai, et al.
Publicado: (2026)
por: Zhai, Jucai, et al.
Publicado: (2026)
Projected Neural Differential Equations for Learning Constrained Dynamics
por: White, Alistair, et al.
Publicado: (2024)
por: White, Alistair, et al.
Publicado: (2024)
PICL: Physics Informed Contrastive Learning for Partial Differential Equations
por: Lorsung, Cooper, et al.
Publicado: (2024)
por: Lorsung, Cooper, et al.
Publicado: (2024)
BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
por: Gorard, Jonathan, et al.
Publicado: (2026)
por: Gorard, Jonathan, et al.
Publicado: (2026)
Physics Informed Token Transformer for Solving Partial Differential Equations
por: Lorsung, Cooper, et al.
Publicado: (2023)
por: Lorsung, Cooper, et al.
Publicado: (2023)
Use What You Know: Causal Foundation Models with Partial Graphs
por: Reuter, Arik, et al.
Publicado: (2026)
por: Reuter, Arik, et al.
Publicado: (2026)
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
por: White, Alistair, et al.
Publicado: (2023)
por: White, Alistair, et al.
Publicado: (2023)
What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection
por: Nguyen, Binh, et al.
Publicado: (2025)
por: Nguyen, Binh, et al.
Publicado: (2025)
Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
por: Negi, Pawan, et al.
Publicado: (2024)
por: Negi, Pawan, et al.
Publicado: (2024)
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
por: Xu, Hao, et al.
Publicado: (2025)
por: Xu, Hao, et al.
Publicado: (2025)
What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities
por: Bahng, Muchang, et al.
Publicado: (2025)
por: Bahng, Muchang, et al.
Publicado: (2025)
Specifying What You Know or Not for Multi-Label Class-Incremental Learning
por: Zhang, Aoting, et al.
Publicado: (2025)
por: Zhang, Aoting, et al.
Publicado: (2025)
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
por: Zheng, Haoyang, et al.
Publicado: (2025)
por: Zheng, Haoyang, et al.
Publicado: (2025)
Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
por: Iyer, Narayan S, et al.
Publicado: (2025)
por: Iyer, Narayan S, et al.
Publicado: (2025)
Planning and Editing What You Retrieve for Enhanced Tool Learning
por: Huang, Tenghao, et al.
Publicado: (2024)
por: Huang, Tenghao, et al.
Publicado: (2024)
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
por: Chattopadhyay, Ashesh, et al.
Publicado: (2021)
por: Chattopadhyay, Ashesh, et al.
Publicado: (2021)
Learn What You Need in Personalized Federated Learning
por: Lv, Kexin, et al.
Publicado: (2024)
por: Lv, Kexin, et al.
Publicado: (2024)
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
por: Li, Zhuohang, et al.
Publicado: (2024)
por: Li, Zhuohang, et al.
Publicado: (2024)
Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
por: Koumpanakis, Michail, et al.
Publicado: (2024)
por: Koumpanakis, Michail, et al.
Publicado: (2024)
What You Read is What You Classify: Highlighting Attributions to Text and Text-Like Inputs
por: Berman, Daniel S., et al.
Publicado: (2026)
por: Berman, Daniel S., et al.
Publicado: (2026)
You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
por: Mąka, Paweł, et al.
Publicado: (2025)
por: Mąka, Paweł, et al.
Publicado: (2025)
What You See is What You Ask: Evaluating Audio Descriptions
por: Kala, Divy, et al.
Publicado: (2025)
por: Kala, Divy, et al.
Publicado: (2025)
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
por: Jin, Heng, et al.
Publicado: (2026)
por: Jin, Heng, et al.
Publicado: (2026)
Optimisation Is Not What You Need
por: Ibias, Alfredo
Publicado: (2025)
por: Ibias, Alfredo
Publicado: (2025)
Lite-SAM Is Actually What You Need for Segment Everything
por: Fu, Jianhai, et al.
Publicado: (2024)
por: Fu, Jianhai, et al.
Publicado: (2024)
Masked Generative Transformer Is What You Need for Image Editing
por: Chow, Wei, et al.
Publicado: (2026)
por: Chow, Wei, et al.
Publicado: (2026)
Position: Model Collapse Does Not Mean What You Think
por: Schaeffer, Rylan, et al.
Publicado: (2025)
por: Schaeffer, Rylan, et al.
Publicado: (2025)
Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
por: Kim, Chanyoung, et al.
Publicado: (2026)
por: Kim, Chanyoung, et al.
Publicado: (2026)
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning
por: von Bassewitz, Jan-Philipp, et al.
Publicado: (2024)
por: von Bassewitz, Jan-Philipp, et al.
Publicado: (2024)
Ejemplares similares
-
What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
por: Wen, Jiawen, et al.
Publicado: (2024) -
What You See is What You Classify: Black Box Attributions
por: Stalder, Steven, et al.
Publicado: (2022) -
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
por: Cai, Will, et al.
Publicado: (2025) -
Generative forecasting with joint probability models
por: Wyrod, Patrick, et al.
Publicado: (2025) -
Tell What You Hear From What You See -- Video to Audio Generation Through Text
por: Liu, Xiulong, et al.
Publicado: (2024)