Maheswaranathan, Niru

1. Deep Unsupervised Learning using Nonequilibrium Thermodynamics
   2015/03/12 by Jascha Sohl‐Dickstein, Sohl-Dickstein, Jascha, Eric A. Weiss +5 · 786 citations
   Computer Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neurons and Cognition (q-bio.NC)
2. Learned Optimizers that Scale and Generalize
   2017/03/14 by Olga Wichrowska, Wichrowska, Olga, Niru Maheswaranathan +11 · 15 citations
   Computer Science · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications #Neural and Evolutionary Computing (cs.NE)
3. Universality and individuality in neural dynamics across large populations of recurrent networks
   2019/07/19 by Niru Maheswaranathan, Maheswaranathan, Niru, Alex H. Williams +7 · 13 citations
   Neuroscience · Computer Science · #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neural Networks and Applications
4. Understanding and correcting pathologies in the training of learned optimizers
   2018/10/24 by Metz, Luke, Maheswaranathan, Niru, Nixon, Jeremy +2 · 5 citations
   #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
5. Tasks, stability, architecture, and compute: Training more effective\n learned optimizers, and using them to train themselves
   2020/09/23 by Luke Metz, Metz, Luke, Niru Maheswaranathan +7 · 4 citations
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #Advanced Neural Network Applications #FOS: Computer and information sciences #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)
6. Practical tradeoffs between memory, compute, and performance in learned optimizers
   2022/03/22 by Luke Metz, C. Daniel Freeman, Metz, Luke +7 · 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 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC)
7. Meta-Learning Update Rules for Unsupervised Representation Learning
   2018/03/31 by Metz, Luke, Maheswaranathan, Niru, Cheung, Brian +1 · 2 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
8. Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
   2019/06/25 by Maheswaranathan, Niru, Williams, Alex, Golub, Matthew D. +2 · 2 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
9. Using a thousand optimization tasks to learn hyperparameter search strategies
   2020/02/27 by Metz, Luke, Maheswaranathan, Niru, Sun, Ruoxi +3 · 2 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
10. Reverse engineering learned optimizers reveals known and novel mechanisms
    2020/11/04 by Maheswaranathan, Niru, Sussillo, David, Metz, Luke +2 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
11. How recurrent networks implement contextual processing in sentiment\n analysis
    2020/04/16 by Niru Maheswaranathan, Maheswaranathan, Niru, David Sussillo +1 · 2 citations
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling
12. The geometry of integration in text classification RNNs
    2020/10/28 by Kyle Aitken, Vinay Ramasesh, Aitken, Kyle +9 · 1 citation
    Computer Science · Neuroscience · #Neural Networks and Applications #Neural Networks and Reservoir Computing #Neural dynamics and brain function
13. Understanding How Encoder-Decoder Architectures Attend
    2021/10/28 by Kyle Aitken, Vinay Ramasesh, Aitken, Kyle +5 · 1 citation
    Neuroscience · #EEG and Brain-Computer Interfaces #Neural dynamics and brain function #Neural and Behavioral Psychology Studies