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05/19/2022
Foundation Posteriors for Approximate Probabilistic Inference
Probabilistic programs provide an expressive representation language for...
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05/16/2022
Constant Power Root Market Makers
The paper introduces a new type of constant function market maker, the c...
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04/26/2022
Know Thy Student: Interactive Learning with Gaussian Processes
Learning often involves interaction between multiple agents. Human teach...
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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...
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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...
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12/10/2021
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study
Contrastive learning has made considerable progress in computer vision, ...
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10/08/2021
Temperature as Uncertainty in Contrastive Learning
Contrastive learning has demonstrated great capability to learn represen...
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08/26/2021
Modeling Item Response Theory with Stochastic Variational Inference
Item Response Theory (IRT) is a ubiquitous model for understanding human...
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07/23/2021
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
High-quality computer science education is limited by the difficulty of ...
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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 ...
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10/26/2020
HarperValleyBank: A Domain-Specific Spoken Dialog Corpus
We introduce HarperValleyBank, a free, public domain spoken dialog corpu...
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10/14/2020
Viewmaker Networks: Learning Views for Unsupervised Representation Learning
Many recent methods for unsupervised representation learning involve tra...
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10/05/2020
A Simple Framework for Uncertainty in Contrastive Learning
Contrastive approaches to representation learning have recently shown gr...
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10/05/2020
Conditional Negative Sampling for Contrastive Learning of Visual Representations
Recent methods for learning unsupervised visual representations, dubbed ...
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05/27/2020
On Mutual Information in Contrastive Learning for Visual Representations
In recent years, several unsupervised, "contrastive" learning algorithms...
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02/01/2020
Variational Item Response Theory: Fast, Accurate, and Expressive
Item Response Theory is a ubiquitous algorithm used around the world to ...
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12/11/2019
Multimodal Generative Models for Compositional Representation Learning
As deep neural networks become more adept at traditional tasks, many of ...
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08/19/2019
Gradient Boosting Machine: A Survey
In this survey, we discuss several different types of gradient boosting ...
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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 ...
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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...
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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...
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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 ...
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02/05/2019
Meta-Amortized Variational Inference and Learning
How can we learn to do probabilistic inference in a way that generalizes...
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10/05/2018
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Stochastic optimization techniques are standard in variational inference...
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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...
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02/14/2018
Multimodal Generative Models for Scalable Weakly-Supervised Learning
Multiple modalities often co-occur when describing natural phenomena. Le...
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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...
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06/14/2016
Spreadsheet Probabilistic Programming
Spreadsheet workbook contents are simple programs. Because of this, prob...
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03/24/2016