Nakkiran, Preetum
- Step-by-Step Diffusion: An Elementary Tutorial
2024/06/13 by Preetum Nakkiran, Nakkiran, Preetum, Arwen Bradley +5 · 10 voices · 1 citation
#cs.LG #cs.AI #cs.CV #stat.ML
- What Algorithms can Transformers Learn? A Study in Length Generalization
2023/10/24 by Hattie Zhou, Zhou, Hattie, Arwen Bradley +13 · 2 voices · 21 citations
Computer Science · #cs.LG #cs.AI #cs.CL #stat.ML
- Deep Double Descent: Where Bigger Models and More Data Hurt
2019/12/04 by Nakkiran, Preetum, Kaplun, Gal, Bansal, Yamini +3 · 43 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
- Revisiting Model Stitching to Compare Neural Representations
2021/06/14 by Yamini Bansal, Preetum Nakkiran, Bansal, Yamini +3 · 26 citations
Computer Science · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Normalizing Flows are Capable Generative Models
2024/12/09 by Shuangfei Zhai, Ruixiang Zhang, Zhai, Shuangfei +18 · 2 voices · 34 citations
Engineering · #Reservoir Engineering and Simulation Methods #cs.CV #cs.LG
- SGD on Neural Networks Learns Functions of Increasing Complexity
2019/05/28 by Preetum Nakkiran, Gal Kaplun, Nakkiran, Preetum +11 · 17 citations
Computer Science · Engineering · #Neural Networks and Applications #Machine Learning and Data Classification #Advanced Data Processing Techniques
- Optimal Regularization Can Mitigate Double Descent
2020/03/04 by Preetum Nakkiran, Prayaag Venkat, Nakkiran, Preetum +5 · 7 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
- Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
2023/09/21 by Błasiok, Jarosław, Nakkiran, Preetum · 10 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Classifier-Free Guidance is a Predictor-Corrector
2024/08/16 by Arwen Bradley, Preetum Nakkiran, Bradley, Arwen +1 · 14 citations
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)
- Limitations of Neural Collapse for Understanding Generalization in Deep Learning
2022/02/17 by Hui, Like, Belkin, Mikhail, Nakkiran, Preetum · 6 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- A Unifying Theory of Distance from Calibration
2022/11/30 by Błasiok, Jarosław, Gopalan, Parikshit, Hu, Lunjia +1 · 8 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
2023/12/07 by Vimal Thilak, Thilak, Vimal, Chen Huang +13 · 9 citations
Computer Science · #Face and Expression Recognition #Machine Learning and Data Classification #Anomaly Detection Techniques and Applications
- Loss Minimization Yields Multicalibration for Large Neural Networks
2023/04/19 by Jarosław Błasiok, Błasiok, Jarosław, Parikshit Gopalan +7 · 1 voice · 5 citations
#cs.LG #cs.AI #stat.ML
- A Formal Framework for Understanding Length Generalization in Transformers
2024/10/03 by Huang, Xinting, Yang, Andy, Bhattamishra, Satwik +5 · 10 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Vanishing Gradients in Reinforcement Finetuning of Language Models
2023/10/31 by Noam Razin, Razin, Noam, Hattie Zhou +13 · 6 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Reinforcement Learning in Robotics #Topic Modeling
- When Does Optimizing a Proper Loss Yield Calibration?
2023/05/30 by Jarosław Błasiok, Parikshit Gopalan, Błasiok, Jarosław +5 · 6 citations
Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Statistics Theory (math.ST)
- Distributional Generalization: A New Kind of Generalization
2020/09/17 by Preetum Nakkiran, Nakkiran, Preetum, Yamini Bansal +1 · 3 citations
Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Statistics Theory (math.ST)
- When is Multicalibration Post-Processing Necessary?
2024/06/10 by Dutch Hansen, Siddartha Devic, Hansen, Dutch +5 · 1 voice · 5 citations
Computer Science · Social Sciences · #Adversarial Robustness in Machine Learning #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #cs.LG
- How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
2024/07/03 by Etai Littwin, Omid Saremi, Littwin, Etai +12 · 1 voice · 5 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications #cs.LG
- More Data Can Hurt for Linear Regression: Sample-wise Double Descent
2019/12/16 by Nakkiran, Preetum · 2 citations
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Statistics Theory (math.ST)
- Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo
2025/02/18 by James Thornton, Thornton, James, Louis Béthune +9 · 5 citations
Computer Science · Engineering · Materials Science · #Advanced Control Systems Optimization #Advanced Mathematical Modeling in Engineering #Catalytic Processes in Materials Science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Adversarial Robustness May Be at Odds With Simplicity
2019/01/02 by Nakkiran, Preetum · 1 citation
#Computational Complexity (cs.CC) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Perspectives on the State and Future of Deep Learning - 2023
2023/12/07 by Micah Goldblum, Goldblum, Micah, Anima Anandkumar +17 · 3 voices
#cs.AI #cs.LG
- Turing-Universal Learners with Optimal Scaling Laws
2021/11/09 by Preetum Nakkiran, Nakkiran, Preetum · 1 citation
Computer Science · #Algorithms and Data Compression #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #Computational Complexity (cs.CC) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Statistics Theory (math.ST)
- Limitations of the NTK for Understanding Generalization in Deep Learning
2022/06/20 by Nikhil Vyas, Vyas, Nikhil, Yamini Bansal +3 · 1 citation
Computer Science · Physics and Astronomy · #Neural Networks and Applications #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference
- APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
2022/10/08 by Elan Rosenfeld, Preetum Nakkiran, Rosenfeld, Elan +7 · 1 citation
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Multimodal Machine Learning Applications
- Mechanisms of Projective Composition of Diffusion Models
2025/02/06 by Arwen Bradley, Preetum Nakkiran, Bradley, Arwen +7 · 1 voice · 2 citations
Computer Science · #Advanced Mathematical Modeling in Engineering
- Deconstructing Distributions: A Pointwise Framework of Learning
2022/02/20 by Gal Kaplun, Nikhil Ghosh, Kaplun, Gal +7 · 1 citation
Computer Science · #Machine Learning and Data Classification #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning
- Trace Length is a Simple Uncertainty Signal in Reasoning Models
2025/10/12 by Devic, Siddartha, Peale, Charlotte, Bradley, Arwen +3 · 3 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences