Hey I'm Bowen! I an M.Eng student in the MIT EECS department with a B.S. degree in EECS and physics (also from MIT). I am currently working in Media Lab's Camera Culture group on meta-modeling techniques for deep learning, but I am also interested in machine learning for health and economics, robotics, and meta-modeling in general.

News

We’ve finally released the MetaQNN Code! Find it here.

I gave a presentation at Google Research - Cambridge on practical CNN meta-modeling. Slides here.

I gave a presentation for the MIT Vision Group on CNN meta-modeling. Slides here.

Publications

Practical Neural Network Performance Prediction for Early Stopping

Bowen Baker*, Otkrist Gupta*, Ramesh Raskar, and Nikhil Naik

Preprint

In the neural network domain, methods for hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of neural network configurations. In this paper, we show that a simple regression model, based on support vector machines, can predict the final performance of partially trained neural network configurations using features based on network architectures, hyperparameters, and time-series validation performance data. We use this regression model to develop an early stopping strategy for neural network configurations. With this early stopping strategy, we obtain significant speedups in both hyperparameter optimization and meta-modeling. Particularly in the context of meta-modeling, our method can learn to predict the performance of drastically different architectures and is seamlessly incorporated into reinforcement learning-based architecture selection algorithms. Finally, we show that our method is simpler, faster, and more accurate than Bayesian methods for learning curve prediction.


Designing neural network architectures using reinforcement learning

Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar

International Conference on Learning Representations, 2017

At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using Q-learning with an ϵ-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks.


Determining the resolution limits of electron-beam lithography: direct measurement of the point-spread function

Vitor R. Manfrinato, Jianguo Wen, Lihua Zhang, Yujia Yang, Richard G. Hobbs, Bowen Baker, Dong Su, Dmitri Zakharov, Nestor J. Zaluzec, Dean J. Miller, Eric A. Stach, and Karl K. Berggren

Nano letters 14, no. 8 (2014): 4406-4412.

One challenge existing since the invention of electron-beam lithography (EBL) is understanding the exposure mechanisms that limit the resolution of EBL. To overcome this challenge, we need to understand the spatial distribution of energy density deposited in the resist, that is, the point-spread function (PSF). During EBL exposure, the processes of electron scattering, phonon, photon, plasmon, and electron emission in the resist are combined, which complicates the analysis of the EBL PSF. Here, we show the measurement of delocalized energy transfer in EBL exposure by using chromatic aberration-corrected energy-filtered transmission electron microscopy (EFTEM) at the sub-10 nm scale. We have defined the role of spot size, electron scattering, secondary electrons, and volume plasmons in the lithographic PSF by performing EFTEM, momentum-resolved electron energy loss spectroscopy (EELS), sub-10 nm EBL, and Monte Carlo simulations. We expect that these results will enable alternative ways to improve the resolution limit of EBL. Furthermore, our approach to study the resolution limits of EBL may be applied to other lithographic techniques where electrons also play a key role in resist exposure, such as ion-beam-, X-ray-, and extreme-ultraviolet lithography.


Industry

Perch

I am a co-founder at Perch. We are an early stage weight room analytics startup and went through the MIT delta v accelerator last summer (2016). I work on machine vision, rep tracking algorithms, and most other aspects of the product back-end.


Quora

I was a Data Science Intern at Quora in the summer of 2015. I worked on identifying and fixing categorically misused topics, improving automated topic labeling, and exploring topic geometries. I also helped in creating metric dashboards, responding to company data inquiries, and fixing bugs in data logging.


AgilOne

I was a Data Science Intern at AgilOne during my 2014 Summer break. I created a framework for validating customer data before running the machine learning models. On top of this, I built a deployment framework that would automatically select features to use and initialize models for new customers. I also did some minor work on the product front end.


Projects

Kinect 2-Chain

The Kinect 2-Chain was a project I worked on for HackMIT 2015. The goal of the project was to aid the visually impaired in navigation. We used a Kinect 2 to map the space in front of the user and send stereo audio signals with varying pitch to indicate the direction and distance of obstacles. We also used a deep learning API so that the user could also request that a description of the scene in front of them be read aloud. We took 2nd place overall and also won the Microsoft prize; some news coverage can be found here.


MIT Robotics Team

I co-founded the MIT Robotics Team in late 2013. I led the software team for 2 years, during which we placed 2nd in the 2014 NASA RASC-AL ROBO-OPS Competition and competed in the 2015 NASA Sample Return Centennial Challenge.