:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Katta, Pavan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2407.01459
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Understanding Emergent Misalignment via Feature Superposition Geometry
by: Minegishi, Gouki, et al.
Published: (2026)

Superposition Yields Robust Neural Scaling
by: Liu, Yizhou, et al.
Published: (2025)

Spectral Superposition: A Theory of Feature Geometry
by: Ivanov, Georgi, et al.
Published: (2026)

Provably Extracting the Features from a General Superposition
by: Liu, Allen
Published: (2025)

Provable Scaling Laws of Feature Emergence from Learning Dynamics of Grokking
by: Tian, Yuandong
Published: (2025)

Mathematical Models of Computation in Superposition
by: Hänni, Kaarel, et al.
Published: (2024)

Superposition in Graph Neural Networks
by: Pertl, Lukas, et al.
Published: (2025)

Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)

Adversarial Attacks Leverage Interference Between Features in Superposition
by: Stevinson, Edward, et al.
Published: (2025)

From Data Statistics to Feature Geometry: How Correlations Shape Superposition
by: Prieto, Lucas, et al.
Published: (2026)

Emergence of Frontier Superposition: Möbius attractor and Cascade Supervision
by: Gu, Hongyu, et al.
Published: (2026)

RanDeS: Randomized Delta Superposition for Multi-Model Compression
by: Zhou, Hangyu, et al.
Published: (2025)

Scaling Law Hypothesis for Multimodal Model
by: Sun, Qingyun, et al.
Published: (2024)

Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)

Scaling Law for Time Series Forecasting
by: Shi, Jingzhe, et al.
Published: (2024)

On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)

Wukong: Towards a Scaling Law for Large-Scale Recommendation
by: Zhang, Buyun, et al.
Published: (2024)

Superposition as Lossy Compression: Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability
by: Bereska, Leonard, et al.
Published: (2025)

Information-Theoretic Foundations for Neural Scaling Laws
by: Jeon, Hong Jun, et al.
Published: (2024)

Time Matters: Scaling Laws for Any Budget
by: Inbar, Itay, et al.
Published: (2024)

Distillation Scaling Laws
by: Busbridge, Dan, et al.
Published: (2025)

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
by: Defilippis, Leonardo, et al.
Published: (2025)

Scaling Laws for Pre-training Agents and World Models
by: Pearce, Tim, et al.
Published: (2024)

Scaling Laws for Precision in High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2026)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice
by: Askari-Hemmat, Reyhane, et al.
Published: (2025)

Does Scaling Law Apply in Time Series Forecasting?
by: Li, Zeyan, et al.
Published: (2025)

Scaling Laws for Data-Efficient Visual Transfer Learning
by: Yang, Wenxuan, et al.
Published: (2025)

Scaling Laws for Imitation Learning in Single-Agent Games
by: Tuyls, Jens, et al.
Published: (2023)

Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
by: Merth, Thomas, et al.
Published: (2024)

What Scales in Cross-Entropy Scaling Law?
by: Yan, Junxi, et al.
Published: (2025)

The Geometry of Multi-Task Grokking: Transverse Instability, Superposition, and Weight Decay Phase Structure
by: Xu, Yongzhong
Published: (2026)

Geometric Limits of Knowledge Distillation: A Minimum-Width Theorem via Superposition Theory
by: Sarkar, Nilesh, et al.
Published: (2026)

Scaling Laws For Scalable Oversight
by: Engels, Joshua, et al.
Published: (2025)

Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)

Towards Embodiment Scaling Laws in Robot Locomotion
by: Ai, Bo, et al.
Published: (2025)

Scaling Laws and In-Context Learning: A Unified Theoretical Framework
by: Mehta, Sushant, et al.
Published: (2025)

Gemstones: A Model Suite for Multi-Faceted Scaling Laws
by: McLeish, Sean, et al.
Published: (2025)

Deriving Neural Scaling Laws from the statistics of natural language
by: Cagnetta, Francesco, et al.
Published: (2026)

Probing for Representation Manifolds in Superposition
by: Modell, Alexander
Published: (2026)

Scaling Attention via Feature Sparsity
by: Xie, Yan, et al.
Published: (2026)