Max Tegmark
16 papers · 2017–2025 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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🗺️ Taxonomy Completionist (21) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
🏆
Keyword Champion
(2)
👑
Triple Crown
🧬
Topic Evolution
🚀
Conference Pioneer
❓
The Questioner
⚡
Prolific Year · Productive
💎
Century Club · Bronze
🗃️
Keyword Collector
(74)
Venues
Top co-authors
Research topics
Machine Learning
(15)
Optimization & Theory · Machine Learning
(11)
Deep Learning
(10)
Core Methods
(10)
Artificial Intelligence
(7)
Core AI
(7)
Architectures
(6)
Optimization & Theory · Deep Learning
(4)
Natural Language Processing
(3)
Resources & Methods
(3)
Learning Types · Deep Learning
(2)
Models
(2)
Mathematics & Optimization
(1)
Learning Types · Machine Learning
(1)
Optimization
(1)
Keywords
sparse autoencoder
(4)
representation learning
(4)
neural network
(4)
neural scaling
(2)
poisson flow
(2)
normalizing flow
(2)
generative model
(2)
in-context learning
(1)
graph theory
(1)
image generation
(1)
hidden layer
(1)
graph modularity
(1)
neural network activation
(1)
diffusion model
(1)
hypothesis testing
(1)
information bottleneck
(1)
recurrent neural network
(1)
spatial representation
(1)
deep neural network
(1)
loss landscape
(1)
Papers
Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
ICML 2025
Efficient Dictionary Learning with Switch Sparse Autoencoders
ICLR 2025
KAN: Kolmogorov–Arnold Networks
ICLR 2025
Low-Rank Adapting Models for Sparse Autoencoders
ICML 2025
Not All Language Model Features Are One-Dimensionally Linear
ICLR 2025
Language Models Represent Space and Time
ICLR 2024
Omnigrok: Grokking Beyond Algorithmic Data
ICLR 2023
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
ICML 2023
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
NeurIPS 2023
The Quantization Model of Neural Scaling
NeurIPS 2023
Poisson Flow Generative Models
NeurIPS 2022
Towards Understanding Grokking: An Effective Theory of Representation Learning
NeurIPS 2022
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
NeurIPS 2020
Learnability for the Information Bottleneck
UAI 2019
The power of deeper networks for expressing natural functions
ICLR 2018
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
ICML 2017