selected research (more)

I’m interested in self-supervised learning, representation learning, curiosity-based exploration, and leveraging internet-scale models and data. I am keen to draw inspiration from intelligence in humans and nature—especially as a goal-post rather than a blueprint. My long-term goal is to develop intelligent agents that can generalize and continually adapt as robustly and efficiently as humans do, allowing them to be safely deployed in the real world.

publications

1. 

   Your Diffusion Model is Secretly a Zero-Shot Classifier

   Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak

   arXiv:cs.LG, 2023

   Abs arXiv Bib PDF Website

   The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focused on sampling; however, diffusion models can also provide conditional density estimates, which are useful for tasks beyond image generation.
   
   In this paper, we show that the density estimates from large-scale text-to-image diffusion models like Stable Diffusion can be leveraged to perform zero-shot classification without any additional training. Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models. Although a gap remains between generative and discriminative approaches on zero-shot recognition tasks, we find that our diffusion-based approach has stronger multimodal relational reasoning abilities than competing discriminative approaches.
   
   Finally, we use Diffusion Classifier to extract standard classifiers from class-conditional diffusion models trained on ImageNet. Even though these diffusion models are trained with weak augmentations and no regularization, we find that they approach the performance of SOTA discriminative ImageNet classifiers. Overall, our strong generalization and robustness results represent an encouraging step toward using generative over discriminative models for downstream tasks.

   @article{li2023diffusion,
     title = {Your Diffusion Model is Secretly a Zero-Shot Classifier},
     author = {Li, Alexander C. and Prabhudesai, Mihir and Duggal, Shivam and Brown, Ellis and Pathak, Deepak},
     year = {2023},
     journal = {arXiv:cs.LG},
     eprint = {2303.16203},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
   }

2. 

   Internet Explorer: Targeted Representation Learning on the Open Web

   Alexander C. Li*, Ellis Brown*, Alexei A. Efros, and Deepak Pathak

   In International Conference on Machine Learning, 2023

   Abs arXiv Bib PDF Website

   Vision models heavily rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only understand knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet—where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30–40 hours.

   @inproceedings{li2023internet,
     title = {Internet Explorer: Targeted Representation Learning on the Open Web},
     author = {Li*, Alexander C. and Brown*, Ellis and Efros, Alexei A. and Pathak, Deepak},
     year = {2023},
     booktitle = {International Conference on Machine Learning},
     organization = {PMLR},
   }

3. 

   An Architecture for Spatiotemporal Template-Based Search

   Ellis Brown, Soobeen Park, Noel Wardord, Adriane Seiffert, Kazuhiko Kawamura, Joseph Lappin, and Maithilee Kunda

   Advances in Cognitive Systems, 2018

   Abs Bib PDF

   Visual search for a spatiotemporal target occurs frequently in human experience—from military or aviation staff monitoring complex displays of multiple moving objects to daycare teachers monitoring a group of children on a playground for risky behaviors. In spatiotemporal search, unlike more traditional visual search tasks, the target cannot be identified from a single frame of visual experience; as the target is a spatiotemporal pattern that unfolds over time, detection of the target must also integrate information over time. We propose a new computational cognitive architecture used to model and understand human visual attention in the specific context of visual search for a spatiotemporal target. Results from a previous human participant study found that humans show interesting attentional capacity limitations in this type of search task. Our architecture, called the SpatioTemporal Template-based Search (STTS) architecture, solves the same search task from the study using a wide variety of parameterized models that each represent a different cognitive theory of visual attention from the psychological literature. We present results from initial computational experiments using STTS as a first step towards understanding the computational nature of attentional bottlenecks in this type of search task, and we discuss how continued STTS experiments will help determine which theoretical models best explain the capacity limitations shown by humans. We expect that results from this research will help refine the design of visual information displays to help human operators perform difficult, real-world monitoring tasks.

   @article{brown2018stts,
     title = {An Architecture for Spatiotemporal Template-Based Search},
     author = {Brown, Ellis and Park, Soobeen and Wardord, Noel and Seiffert, Adriane and Kawamura, Kazuhiko and Lappin, Joseph and Kunda, Maithilee},
     year = {2018},
     journal = {Advances in Cognitive Systems},
     volume = {6},
     pages = {101--118},
   }

talks

1. 

   Linearly Constrained Separable Optimization

   Ellis Brown, Nicholas Moehle, and Mykel J. Kochenderfer

   In JuliaCon 2021 JuMP Track, Jul 2021

   Abs Blog Video

   Many optimization problems involve minimizing a sum of univariate functions, each with a different variable, subject to coupling constraints. We present PiecewiseQuadratics.jl and SeparableOptimization.jl, two Julia packages for solving such problems when these univariate functions in the objective are piecewise-quadratic.

2. AISES-19

   Modeling Uncertainty in Bayesian Neural Networks with Dropout: The effect of weight prior and network architecture selection

   Ellis Brown*, Melanie Manko*, and Ethan Matlin*

   In American Indian Science and Engineering Society National Conference, Oct 2019

   🎖️ Third Place, Graduate Student Research Competition

   Abs Poster

   While neural networks are quite successful at making predictions, these predictions are usually point estimates lacking any notion of uncertainty. However, when fed data very different from its training data, it is useful for a neural network to realize that its predictions could very well be wrong and encode that information through uncertainty bands around its point estimate prediction. Bayesian Neural Networks trained with Dropout are a natural way of modeling this uncertainty with theoretical foundations relating them to Variational Inference approximating Gaussian Process posteriors. In this paper, we investigate the effects of weight prior selection and network architecture on uncertainty estimates derived from Dropout Bayesian Neural Networks.

3. AISES-17

   Computational Cognitive Systems to Model Information Salience

   Ellis Brown, Adriane Seiffert, Noel Warford, Soobeen Park, and Maithilee Kunda

   In American Indian Science and Engineering Society National Conference, Sep 2017

   Abs Slides Website

   This project seeks to model human information salience—in this case, how much a person will notice a piece of visual information—using an approach from artificial intelligence that involves building computational cognitive systems models of human performance on certain tasks. Too much visual information can be overwhelming, making it difficult for people to discern the important parts. This can be detrimental in many situations where visual attention is crucial, such as air traffic control systems, military information displays, or TSA screening stations. Results from a previous human participant study found that humans show interesting limitations in attentional capacity when asked to monitor a complex moving display. We create artificial computational agents based on various cognitive models of visual attention to solve the same visual information salience task and run computational experiments to measure cognitive performance. In follow-up work, we will compare the performance of our models to the human data to determine which models best explain the capacity limitations shown by people.

code