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Disentanglement with Biological Constraints: A Theory of Functional Cell
  Types
v1v2 (latest)

Disentanglement with Biological Constraints: A Theory of Functional Cell Types

International Conference on Learning Representations (ICLR), 2022
30 September 2022
James C. R. Whittington
W. Dorrell
Surya Ganguli
Timothy Edward John Behrens
ArXiv (abs)PDFHTML

Papers citing "Disentanglement with Biological Constraints: A Theory of Functional Cell Types"

25 / 25 papers shown
Vector Quantization in the Brain: Grid-like Codes in World Models
Vector Quantization in the Brain: Grid-like Codes in World Models
Xiangyuan Peng
Xingsi Dong
Si Wu
143
0
0
16 Oct 2025
A mathematical theory for understanding when abstract representations emerge in neural networks
A mathematical theory for understanding when abstract representations emerge in neural networks
Bin Wang
W. Jeffrey Johnston
Stefano Fusi
101
0
0
10 Oct 2025
Information Must Flow: Recursive Bootstrapping for Information Bottleneck in Optimal Transport
Information Must Flow: Recursive Bootstrapping for Information Bottleneck in Optimal Transport
Xin Li
212
2
0
08 Jul 2025
Zero-Shot Visual Generalization in Robot Manipulation
Zero-Shot Visual Generalization in Robot Manipulation
Sumeet Batra
Gaurav Sukhatme
228
3
0
16 May 2025
A Controllable Appearance Representation for Flexible Transfer and Editing
A Controllable Appearance Representation for Flexible Transfer and Editing
Santiago Jimenez-Navarro
Julia Guerrero-Viu
B. Masiá
DiffM
341
0
0
21 Apr 2025
Disentanglement and Compositionality of Letter Identity and Letter
  Position in Variational Auto-Encoder Vision Models
Disentanglement and Compositionality of Letter Identity and Letter Position in Variational Auto-Encoder Vision Models
Bruno Bianchi
Aakash Agrawal
S. Dehaene
Emmanuel Chemla
Yair Lakretz
DRLCoGe
350
0
0
11 Dec 2024
Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory
  Cortex
Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex
Tanishq Kumar
Blake Bordelon
Cengiz Pehlevan
Venkatesh N. Murthy
Samuel Gershman
OODCLLSSL
376
0
0
05 Nov 2024
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation
Sumeet Batra
Gaurav Sukhatme
OffRLDRL
275
3
0
09 Oct 2024
Spatial embedding promotes a specific form of modularity with low
  entropy and heterogeneous spectral dynamics
Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
Cornelia Sheeran
Andrew S. Ham
D. Astle
Jascha Achterberg
Danyal Akarca
174
6
0
26 Sep 2024
Disentanglement with Factor Quantized Variational Autoencoders
Disentanglement with Factor Quantized Variational Autoencoders
Gulcin Baykal
M. Kandemir
Gözde B. Ünal
CoGeDRL
317
1
0
23 Sep 2024
Residual Stream Analysis with Multi-Layer SAEs
Residual Stream Analysis with Multi-Layer SAEsInternational Conference on Learning Representations (ICLR), 2024
Tim Lawson
Lucy Farnik
Conor Houghton
Laurence Aitchison
377
11
0
06 Sep 2024
Disentangled Representations for Causal Cognition
Disentangled Representations for Causal Cognition
Filippo Torresan
Manuel Baltieri
CML
264
4
0
30 Jun 2024
Binding in hippocampal-entorhinal circuits enables compositionality in
  cognitive maps
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps
Christopher J. Kymn
Sonia Mazelet
Anthony Thomas
Denis Kleyko
E. P. Frady
Friedrich T. Sommer
Bruno A. Olshausen
264
10
0
27 Jun 2024
When does compositional structure yield compositional generalization? A kernel theory
When does compositional structure yield compositional generalization? A kernel theory
Samuel Lippl
Kim Stachenfeld
NAICoGe
623
14
0
26 May 2024
Poisson Variational Autoencoder
Poisson Variational AutoencoderNeural Information Processing Systems (NeurIPS), 2024
Hadi Vafaii
Dekel Galor
Jacob L. Yates
DRL
290
6
0
23 May 2024
Mechanistic Interpretability for AI Safety -- A Review
Mechanistic Interpretability for AI Safety -- A Review
Leonard Bereska
E. Gavves
AI4CE
339
301
0
22 Apr 2024
Tripod: Three Complementary Inductive Biases for Disentangled
  Representation Learning
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Kyle Hsu
Jubayer Ibn Hamid
Kaylee Burns
Chelsea Finn
Jiajun Wu
CML
247
11
0
16 Apr 2024
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity
  for Abstract Visual Reasoning
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
Ruiqian Nai
Zixin Wen
Ji Li
Yuanzhi Li
Yang Gao
183
2
0
01 Mar 2024
Probing Biological and Artificial Neural Networks with Task-dependent
  Neural Manifolds
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Michael Kuoch
Chi-Ning Chou
Nikhil Parthasarathy
Joel Dapello
J. DiCarlo
H. Sompolinsky
SueYeon Chung
198
3
0
21 Dec 2023
Evolutionary algorithms as an alternative to backpropagation for
  supervised training of Biophysical Neural Networks and Neural ODEs
Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
James Hazelden
Yuhan Helena Liu
Eli Shlizerman
E. Shea-Brown
372
6
0
17 Nov 2023
Flow Factorized Representation Learning
Flow Factorized Representation LearningNeural Information Processing Systems (NeurIPS), 2023
Yue Song
Thomas Anderson Keller
Andrii Zadaianchuk
Max Welling
DRLOOD
334
5
0
22 Sep 2023
Correlative Information Maximization: A Biologically Plausible Approach
  to Supervised Deep Neural Networks without Weight Symmetry
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight SymmetryNeural Information Processing Systems (NeurIPS), 2023
Bariscan Bozkurt
Cengiz Pehlevan
A. Erdogan
283
4
0
07 Jun 2023
Disentanglement via Latent Quantization
Disentanglement via Latent QuantizationNeural Information Processing Systems (NeurIPS), 2023
Kyle Hsu
W. Dorrell
James C. R. Whittington
Jiajun Wu
Chelsea Finn
DRL
382
36
0
28 May 2023
The Lie-Group Bayesian Learning Rule
The Lie-Group Bayesian Learning RuleInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
E. M. Kıral
Thomas Möllenhoff
Mohammad Emtiyaz Khan
BDL
177
5
0
08 Mar 2023
Bottom-up and top-down approaches for the design of neuromorphic
  processing systems: Tradeoffs and synergies between natural and artificial
  intelligence
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligenceProceedings of the IEEE (Proc. IEEE), 2021
Charlotte Frenkel
D. Bol
Giacomo Indiveri
279
59
0
02 Jun 2021
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