Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
Fuente:
arXiv
Saved in:
| Main Authors: | Schaeffer, Rylan, Lecomte, Victor, Pai, Dhruv Bhandarkar, Carranza, Andres, Isik, Berivan, Unell, Alyssa, Khona, Mikail, Yerxa, Thomas, LeCun, Yann, Chung, SueYeon, Gromov, Andrey, Shwartz-Ziv, Ravid, Koyejo, Sanmi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Video Representation Learning with Joint-Embedding Predictive Architectures
by: Drozdov, Katrina, et al.
Published: (2024)
by: Drozdov, Katrina, et al.
Published: (2024)
AI Must Embrace Specialization via Superhuman Adaptable Intelligence
by: Goldfeder, Judah, et al.
Published: (2026)
by: Goldfeder, Judah, et al.
Published: (2026)
The Entropy Enigma: Success and Failure of Entropy Minimization
by: Press, Ori, et al.
Published: (2024)
by: Press, Ori, et al.
Published: (2024)
Does Representation Matter? Exploring Intermediate Layers in Large Language Models
by: Skean, Oscar, et al.
Published: (2024)
by: Skean, Oscar, et al.
Published: (2024)
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
by: Lecomte, Victor, et al.
Published: (2023)
by: Lecomte, Victor, et al.
Published: (2023)
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
by: Shani, Chen, et al.
Published: (2025)
by: Shani, Chen, et al.
Published: (2025)
On Training in Imagination
by: Timor, Nadav, et al.
Published: (2026)
by: Timor, Nadav, et al.
Published: (2026)
Variance-Covariance Regularization Improves Representation Learning
by: Zhu, Jiachen, et al.
Published: (2023)
by: Zhu, Jiachen, et al.
Published: (2023)
Pretraining Scaling Laws for Generative Evaluations of Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Frontier AI Models
by: Duan, Sunny, et al.
Published: (2024)
by: Duan, Sunny, et al.
Published: (2024)
Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
by: Peetathawatchai, Pura, et al.
Published: (2024)
by: Peetathawatchai, Pura, et al.
Published: (2024)
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
by: Shwartz-Ziv, Ravid, et al.
Published: (2023)
by: Shwartz-Ziv, Ravid, et al.
Published: (2023)
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation
by: Zeevi, Tal, et al.
Published: (2024)
by: Zeevi, Tal, et al.
Published: (2024)
On Fairness of Low-Rank Adaptation of Large Models
by: Ding, Zhoujie, et al.
Published: (2024)
by: Ding, Zhoujie, et al.
Published: (2024)
Learning to Compress: Local Rank and Information Compression in Deep Neural Networks
by: Patel, Niket, et al.
Published: (2024)
by: Patel, Niket, et al.
Published: (2024)
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
by: Panda, Ashwinee, et al.
Published: (2024)
by: Panda, Ashwinee, et al.
Published: (2024)
Adaptive Compression in Federated Learning via Side Information
by: Isik, Berivan, et al.
Published: (2023)
by: Isik, Berivan, et al.
Published: (2023)
Scaling Laws for Downstream Task Performance of Large Language Models
by: Isik, Berivan, et al.
Published: (2024)
by: Isik, Berivan, et al.
Published: (2024)
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)
by: Gupta, Isha, et al.
Published: (2025)
JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention
by: Ioannides, Georgios, et al.
Published: (2025)
by: Ioannides, Georgios, et al.
Published: (2025)
Just How Flexible are Neural Networks in Practice?
by: Shwartz-Ziv, Ravid, et al.
Published: (2024)
by: Shwartz-Ziv, Ravid, et al.
Published: (2024)
Layer by Layer: Uncovering Hidden Representations in Language Models
by: Skean, Oscar, et al.
Published: (2025)
by: Skean, Oscar, et al.
Published: (2025)
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
by: Arefin, Md Rifat, et al.
Published: (2024)
by: Arefin, Md Rifat, et al.
Published: (2024)
Statistical Mechanics of Support Vector Regression
by: Canatar, Abdulkadir, et al.
Published: (2024)
by: Canatar, Abdulkadir, et al.
Published: (2024)
Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures
by: Ioannides, Georgios, et al.
Published: (2026)
by: Ioannides, Georgios, et al.
Published: (2026)
Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin
by: Queipo-de-Llano, Enrique, et al.
Published: (2025)
by: Queipo-de-Llano, Enrique, et al.
Published: (2025)
TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records
by: Cui, Hejie, et al.
Published: (2025)
by: Cui, Hejie, et al.
Published: (2025)
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)
by: Obbad, Elyas, et al.
Published: (2024)
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023)
by: Miranda, Brando, et al.
Published: (2023)
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)
by: Jiang, Minhao, et al.
Published: (2024)
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
by: Kazdan, Joshua, et al.
Published: (2024)
by: Kazdan, Joshua, et al.
Published: (2024)
Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
by: Gerstgrasser, Matthias, et al.
Published: (2024)
by: Gerstgrasser, Matthias, et al.
Published: (2024)
Linear Readout of Neural Manifolds with Continuous Variables
by: Slatton, Will, et al.
Published: (2026)
by: Slatton, Will, et al.
Published: (2026)
Spectral Analysis of Representational Similarity with Limited Neurons
by: Kang, Hyunmo, et al.
Published: (2025)
by: Kang, Hyunmo, et al.
Published: (2025)
Similar Items
-
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024) -
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024) -
Video Representation Learning with Joint-Embedding Predictive Architectures
by: Drozdov, Katrina, et al.
Published: (2024) -
AI Must Embrace Specialization via Superhuman Adaptable Intelligence
by: Goldfeder, Judah, et al.
Published: (2026) -
The Entropy Enigma: Success and Failure of Entropy Minimization
by: Press, Ori, et al.
Published: (2024)