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Researcher profile

Mohammad Norouzi

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Memory Augmented Policy Optimization for Program Synthesis with Generalization

    2018 · arXiv (Cornell University)

    This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. …

  2. RL Unplugged: Benchmarks for Offline Reinforcement Learning.

    2020 · arXiv (Cornell University)

    Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with …

  3. Big Self-Supervised Models Advance Medical Image Classification

    2021 · arXiv (Cornell University)

    Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning …

  4. Reward Augmented Maximum Likelihood for Neural Structured Prediction

    2016 · arXiv (Cornell University)

    A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a …

  5. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    2016 · arXiv (Cornell University)

    Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive …

  6. QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

    2018 · arXiv (Cornell University)

    Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the …