Saved in:
| Main Authors: | Ram, Parikshit, Clarkson, Kenneth L., Klinger, Tim, Ubaru, Shashanka, Gray, Alexander G. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14095 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
What makes Models Compositional? A Theoretical View: With Supplement
by: Ram, Parikshit, et al.
Published: (2024)
by: Ram, Parikshit, et al.
Published: (2024)
Compositional Program Generation for Few-Shot Systematic Generalization
by: Klinger, Tim, et al.
Published: (2023)
by: Klinger, Tim, et al.
Published: (2023)
Finding Clustering Algorithms in the Transformer Architecture
by: Clarkson, Kenneth L., et al.
Published: (2025)
by: Clarkson, Kenneth L., et al.
Published: (2025)
Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery
by: Hoyos, Paulina, et al.
Published: (2026)
by: Hoyos, Paulina, et al.
Published: (2026)
Learning interpretable positional encodings in transformers depends on initialization
by: Ito, Takuya, et al.
Published: (2024)
by: Ito, Takuya, et al.
Published: (2024)
Multivariate trace estimation using quantum state space linear algebra
by: Yosef, Liron Mor, et al.
Published: (2024)
by: Yosef, Liron Mor, et al.
Published: (2024)
Topological data analysis on noisy quantum computers
by: Akhalwaya, Ismail Yunus, et al.
Published: (2022)
by: Akhalwaya, Ismail Yunus, et al.
Published: (2022)
Quantifying artificial intelligence through algorithmic generalization
by: Ito, Takuya, et al.
Published: (2024)
by: Ito, Takuya, et al.
Published: (2024)
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by: Mukherjee, Arpan, et al.
Published: (2024)
by: Mukherjee, Arpan, et al.
Published: (2024)
Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations
by: Carrow, Stephen, et al.
Published: (2024)
by: Carrow, Stephen, et al.
Published: (2024)
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
by: Shustin, Paz Fink, et al.
Published: (2022)
by: Shustin, Paz Fink, et al.
Published: (2022)
Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
by: Nartallo-Kaluarachchi, Ramón, et al.
Published: (2025)
by: Nartallo-Kaluarachchi, Ramón, et al.
Published: (2025)
Learning to Focus: Focal Attention for Selective and Scalable Transformers
by: Ram, Dhananjay, et al.
Published: (2025)
by: Ram, Dhananjay, et al.
Published: (2025)
On Learning Representations for Tabular Data Distillation
by: Kang, Inwon, et al.
Published: (2025)
by: Kang, Inwon, et al.
Published: (2025)
Modern Methods in Associative Memory
by: Krotov, Dmitry, et al.
Published: (2025)
by: Krotov, Dmitry, et al.
Published: (2025)
On Efficient Solutions of General Structured Markov Processes in Quantum Computational Environments
by: Kalantzis, Vasileios, et al.
Published: (2024)
by: Kalantzis, Vasileios, et al.
Published: (2024)
Randomized matrix-free quadrature: unified and uniform bounds for stochastic Lanczos quadrature and the kernel polynomial method
by: Chen, Tyler, et al.
Published: (2022)
by: Chen, Tyler, et al.
Published: (2022)
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
by: Kang, Inwon, et al.
Published: (2026)
by: Kang, Inwon, et al.
Published: (2026)
Dense Associative Memory with Epanechnikov Energy
by: Hoover, Benjamin, et al.
Published: (2025)
by: Hoover, Benjamin, et al.
Published: (2025)
Balancing Multi-modal Sensor Learning via Multi-objective Optimization
by: Fernando, Heshan, et al.
Published: (2025)
by: Fernando, Heshan, et al.
Published: (2025)
Dense Associative Memory Through the Lens of Random Features
by: Hoover, Benjamin, et al.
Published: (2024)
by: Hoover, Benjamin, et al.
Published: (2024)
Deep Clustering with Associative Memories
by: Saha, Bishwajit, et al.
Published: (2026)
by: Saha, Bishwajit, et al.
Published: (2026)
Distribution Free Prediction Sets for Node Classification
by: Clarkson, Jase
Published: (2022)
by: Clarkson, Jase
Published: (2022)
What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
by: Helbling, Alec, et al.
Published: (2026)
by: Helbling, Alec, et al.
Published: (2026)
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
by: Jia, Jinghan, et al.
Published: (2024)
by: Jia, Jinghan, et al.
Published: (2024)
Beyond Sliding Windows: Learning to Manage Memory in Non-Markovian Environments
by: Tasse, Geraud Nangue, et al.
Published: (2025)
by: Tasse, Geraud Nangue, et al.
Published: (2025)
Understanding Self-Supervised Learning via Gaussian Mixture Models
by: Bansal, Parikshit, et al.
Published: (2024)
by: Bansal, Parikshit, et al.
Published: (2024)
Transformers Can Learn Rules They've Never Seen: Proof of Computation Beyond Interpolation
by: Gray, Andy
Published: (2026)
by: Gray, Andy
Published: (2026)
Enhancing In-context Learning via Linear Probe Calibration
by: Abbas, Momin, et al.
Published: (2024)
by: Abbas, Momin, et al.
Published: (2024)
Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion
by: Lebedev, Anton, et al.
Published: (2025)
by: Lebedev, Anton, et al.
Published: (2025)
Context-Free Synthetic Data Mitigates Forgetting
by: Bansal, Parikshit, et al.
Published: (2025)
by: Bansal, Parikshit, et al.
Published: (2025)
On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
by: Alam, Md Ibrahim Ibne, et al.
Published: (2024)
by: Alam, Md Ibrahim Ibne, et al.
Published: (2024)
500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)
by: Majumder, Suvodeep, et al.
Published: (2018)
by: Majumder, Suvodeep, et al.
Published: (2018)
Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems
by: Li, Hongbo, et al.
Published: (2025)
by: Li, Hongbo, et al.
Published: (2025)
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
by: Pareek, Parikshit, et al.
Published: (2023)
by: Pareek, Parikshit, et al.
Published: (2023)
Enabling Approximate Joint Sampling in Diffusion LMs
by: Bansal, Parikshit, et al.
Published: (2025)
by: Bansal, Parikshit, et al.
Published: (2025)
HashAttention: Semantic Sparsity for Faster Inference
by: Desai, Aditya, et al.
Published: (2024)
by: Desai, Aditya, et al.
Published: (2024)
Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspective
by: Fernando, Heshan, et al.
Published: (2024)
by: Fernando, Heshan, et al.
Published: (2024)
Model Sparsity Can Simplify Machine Unlearning
by: Jia, Jinghan, et al.
Published: (2023)
by: Jia, Jinghan, et al.
Published: (2023)
Decoupled-Value Attention for Prior-Data Fitted Networks: GP Inference for Physical Equations
by: Sharma, Kaustubh, et al.
Published: (2025)
by: Sharma, Kaustubh, et al.
Published: (2025)
Similar Items
-
What makes Models Compositional? A Theoretical View: With Supplement
by: Ram, Parikshit, et al.
Published: (2024) -
Compositional Program Generation for Few-Shot Systematic Generalization
by: Klinger, Tim, et al.
Published: (2023) -
Finding Clustering Algorithms in the Transformer Architecture
by: Clarkson, Kenneth L., et al.
Published: (2025) -
Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery
by: Hoyos, Paulina, et al.
Published: (2026) -
Learning interpretable positional encodings in transformers depends on initialization
by: Ito, Takuya, et al.
Published: (2024)