David Klindt

1. Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse\n Coding
   2020/07/21 by David Klindt, Klindt, David, Lukas Schott +11 · 12 citations
   Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML)
2. Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
   2024/06/10 by Daniel Kunin, Allan Raventós, Kunin, Daniel +11 · 4 voices · 6 citations
   Computer Science · #Machine Learning and Data Classification
3. Cross-Entropy Is All You Need To Invert the Data Generating Process
   2024/10/29 by Patrik Reizinger, Alice Bizeul, Reizinger, Patrik +11 · 10 citations
   Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
4. Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
   2024/11/20 by Charles O'Neill, Charles O’Neill, Alim Gumran +4 · 1 voice · 5 citations
   Computer Science · #Generative Adversarial Networks and Image Synthesis #Neural Networks and Applications #cs.LG
5. From superposition to sparse codes: interpretable representations in neural networks
   2025/03/03 by David Klindt, Charles O'Neill, Charles O’Neill +8 · 1 voice · 4 citations
   Computer Science · #Neural Networks and Applications #cs.LG
6. Latent computing by biological neural networks: A dynamical systems framework
   2025/02/20 by Fatih Dinc, Marta Blanco-Pozo, Dinc, Fatih +19 · 2 voices
   #q-bio.NC
7. Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
   2022/10/06 by Martin Bjerke, Lukas Schott, Bjerke, Martin +9 · 1 citation
   Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC)
8. Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
   2025/04/17 by Patrik Reizinger, Reizinger, Patrik, Randall Balestriero +5 · 1 voice · 1 citation
   Social Sciences · Psychology · #cs.LG #cs.AI #stat.ML
9. A unifying framework from neural superposition to sparse interpretable codes
   2026/07/14 by David Klindt, Charles O’Neill, Patrik Reizinger +2 · 2 voices
   Neuroscience · Computer Science · #Face Recognition and Perception #Explainable Artificial Intelligence (XAI) #Embodied and Extended Cognition