:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Mohan, Arvind, Chattopadhyay, Ashesh, Miller, Jonah
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Physics
Online Access:	https://arxiv.org/abs/2411.15101
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
by: Wen, Jiawen, et al.
Published: (2024)

What You See is What You Classify: Black Box Attributions
by: Stalder, Steven, et al.
Published: (2022)

Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
by: Cai, Will, et al.
Published: (2025)

Generative forecasting with joint probability models
by: Wyrod, Patrick, et al.
Published: (2025)

Tell What You Hear From What You See -- Video to Audio Generation Through Text
by: Liu, Xiulong, et al.
Published: (2024)

What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
by: Trevithick, Alex, et al.
Published: (2024)

LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
by: Guan, Haiwen, et al.
Published: (2024)

LUCIE-3D: A three-dimensional climate emulator for forced responses
by: Guan, Haiwen, et al.
Published: (2025)

Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
by: Liu, Zhaoyang, et al.
Published: (2025)

More Compute Is What You Need
by: Guo, Zhen
Published: (2024)

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
by: Chattopadhyay, Ashesh, et al.
Published: (2021)

What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection
by: Nguyen, Binh, et al.
Published: (2025)

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
by: Heimbach, Lothar, et al.
Published: (2025)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
by: Zhai, Jucai, et al.
Published: (2026)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
by: Gorard, Jonathan, et al.
Published: (2026)

What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities
by: Bahng, Muchang, et al.
Published: (2025)

Projected Neural Differential Equations for Learning Constrained Dynamics
by: White, Alistair, et al.
Published: (2024)

Use What You Know: Causal Foundation Models with Partial Graphs
by: Reuter, Arik, et al.
Published: (2026)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
by: Negi, Pawan, et al.
Published: (2024)

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
by: Xu, Hao, et al.
Published: (2025)

Specifying What You Know or Not for Multi-Label Class-Incremental Learning
by: Zhang, Aoting, et al.
Published: (2025)

Planning and Editing What You Retrieve for Enhanced Tool Learning
by: Huang, Tenghao, et al.
Published: (2024)

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
by: White, Alistair, et al.
Published: (2023)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

Learn What You Need in Personalized Federated Learning
by: Lv, Kexin, et al.
Published: (2024)

What You Read is What You Classify: Highlighting Attributions to Text and Text-Like Inputs
by: Berman, Daniel S., et al.
Published: (2026)

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
by: Mąka, Paweł, et al.
Published: (2025)

Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
by: Li, Zhuohang, et al.
Published: (2024)

What You See is What You Ask: Evaluating Audio Descriptions
by: Kala, Divy, et al.
Published: (2025)

Optimisation Is Not What You Need
by: Ibias, Alfredo
Published: (2025)

Head for the Edge: Getting What You Ask For.
by: Johnson, Doug
Published: (1998)

Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
by: Koumpanakis, Michail, et al.
Published: (2024)

Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
by: Iyer, Narayan S, et al.
Published: (2025)

Lite-SAM Is Actually What You Need for Segment Everything
by: Fu, Jianhai, et al.
Published: (2024)

Masked Generative Transformer Is What You Need for Image Editing
by: Chow, Wei, et al.
Published: (2026)

Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)

Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
by: Jin, Heng, et al.
Published: (2026)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)