b'Mike Wu'

research

∙ 05/19/2022

Foundation Posteriors for Approximate Probabilistic Inference

Probabilistic programs provide an expressive representation language for...

7 Mike Wu, et al. ∙

research

∙ 05/16/2022

Constant Power Root Market Makers

The paper introduces a new type of constant function market maker, the c...

0 Mike Wu, et al. ∙

research

∙ 04/26/2022

Know Thy Student: Interactive Learning with Gaussian Processes

Learning often involves interaction between multiple agents. Human teach...

5 Rose E. Wang, et al. ∙

research

∙ 04/01/2022

Differentiating small-scale subhalo distributions in CDM and WDM models using persistent homology

The spatial distribution of galaxies at sufficiently small scales will e...

0 Jessi Cisewski-Kehe, et al. ∙

research

∙ 01/18/2022

Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash

A common misconception among blockchain users is that pseudonymity guara...

3 Mike Wu, et al. ∙

research

∙ 12/10/2021

Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study

Contrastive learning has made considerable progress in computer vision, ...

19 Ananya Karthik, et al. ∙

research

∙ 10/08/2021

Temperature as Uncertainty in Contrastive Learning

Contrastive learning has demonstrated great capability to learn represen...

3 Oliver Zhang, et al. ∙

research

∙ 08/26/2021

Modeling Item Response Theory with Stochastic Variational Inference

Item Response Theory (IRT) is a ubiquitous model for understanding human...

15 Mike Wu, et al. ∙

research

∙ 07/23/2021

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

High-quality computer science education is limited by the difficulty of ...

3 Mike Wu, et al. ∙

research

∙ 06/01/2021

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

In traditional software programs, we take for granted how easy it is to ...

12 Mike Wu, et al. ∙

research

∙ 10/26/2020

HarperValleyBank: A Domain-Specific Spoken Dialog Corpus

We introduce HarperValleyBank, a free, public domain spoken dialog corpu...

9 Mike Wu, et al. ∙

research

∙ 10/14/2020

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Many recent methods for unsupervised representation learning involve tra...

9 Alex Tamkin, et al. ∙

research

∙ 10/05/2020

A Simple Framework for Uncertainty in Contrastive Learning

Contrastive approaches to representation learning have recently shown gr...

15 Mike Wu, et al. ∙

research

∙ 10/05/2020

Conditional Negative Sampling for Contrastive Learning of Visual Representations

Recent methods for learning unsupervised visual representations, dubbed ...

7 Mike Wu, et al. ∙

research

∙ 05/27/2020

On Mutual Information in Contrastive Learning for Visual Representations

In recent years, several unsupervised, "contrastive" learning algorithms...

0 Mike Wu, et al. ∙

research

∙ 02/01/2020

Variational Item Response Theory: Fast, Accurate, and Expressive

Item Response Theory is a ubiquitous algorithm used around the world to ...

7 Mike Wu, et al. ∙

research

∙ 12/11/2019

Multimodal Generative Models for Compositional Representation Learning

As deep neural networks become more adept at traditional tasks, many of ...

29 Mike Wu, et al. ∙

research

∙ 08/19/2019

Gradient Boosting Machine: A Survey

In this survey, we discuss several different types of gradient boosting ...

0 Zhiyuan He, et al. ∙

research

∙ 08/14/2019

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

Deep models have advanced prediction in many domains, but their lack of ...

0 Mike Wu, et al. ∙

research

∙ 08/13/2019

Regional Tree Regularization for Interpretability in Black Box Models

The lack of interpretability remains a barrier to the adoption of deep n...

5 Mike Wu, et al. ∙

research

∙ 05/23/2019

Generative Grading: Neural Approximate Parsing for Automated Student Feedback

Open access to high-quality education is limited by the difficulty of pr...

0 Ali Malik, et al. ∙

research

∙ 03/11/2019

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication

Visual modes of communication are ubiquitous in modern life — from maps ...

0 Judith Fan, et al. ∙

research

∙ 02/05/2019

Meta-Amortized Variational Inference and Learning

How can we learn to do probabilistic inference in a way that generalizes...

26 Kristy Choi, et al. ∙

research

∙ 10/05/2018

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Stochastic optimization techniques are standard in variational inference...

6 Mike Wu, et al. ∙

research

∙ 09/05/2018

Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

In modern computer science education, massive open online courses (MOOCs...

0 Mike Wu, et al. ∙

research

∙ 02/14/2018

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Multiple modalities often co-occur when describing natural phenomena. Le...

0 Mike Wu, et al. ∙

research

∙ 11/16/2017

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of de...

0 Mike Wu, et al. ∙

research

∙ 06/14/2016

Spreadsheet Probabilistic Programming

Spreadsheet workbook contents are simple programs. Because of this, prob...

0 Mike Wu, et al. ∙

research

∙ 03/24/2016

Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow

The proposed method uses live image footage which, based on calculations...

0 Stephen Yu, et al. ∙

Mike Wu

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