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06/15/2023
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts
Lectures are a learning experience for both students and teachers. Stude...
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06/12/2023
Generating Language Corrections for Teaching Physical Control Tasks
AI assistance continues to help advance applications in education, from ...
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04/17/2023
Learning to Compress Prompts with Gist Tokens
Prompting is now the primary way to utilize the multitask capabilities o...
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02/11/2023
Multispectral Self-Supervised Learning with Viewmaker Networks
Contrastive learning methods have been applied to a range of domains and...
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12/20/2022
Task Ambiguity in Humans and Language Models
Language models have recently achieved strong performance across a wide ...
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12/16/2022
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
What role do augmentations play in contrastive learning? Recent work sug...
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11/25/2022
Assistive Teaching of Motor Control Tasks to Humans
Recent works on shared autonomy and assistive-AI technologies, such as a...
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11/16/2022
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
Humans tame the complexity of mathematical reasoning by developing hiera...
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09/16/2022
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models
Probabilistic models of language understanding are interpretable and str...
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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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05/03/2022
Mixed-effects transformers for hierarchical adaptation
Language use differs dramatically from context to context. To some degre...
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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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04/18/2022
Active Learning Helps Pretrained Models Learn the Intended Task
Models can fail in unpredictable ways during deployment due to task ambi...
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03/21/2022
Language modeling via stochastic processes
Modern language models can generate high-quality short texts. However, t...
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02/17/2022
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when r...
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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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11/23/2021
DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
Self-supervised learning algorithms, including BERT and SimCLR, have ena...
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10/19/2021
Open-domain clarification question generation without question examples
An overarching goal of natural language processing is to enable machines...
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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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06/16/2021
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
Abstract symbolic reasoning, as required in domains such as mathematics ...
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06/08/2021
Question Generation for Adaptive Education
Intelligent and adaptive online education systems aim to make high-quali...
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06/04/2021
Emergent Communication of Generalizations
To build agents that can collaborate effectively with others, recent res...
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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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11/09/2020
Language Through a Prism: A Spectral Approach for Multiscale Language Representations
Language exhibits structure at different scales, ranging from subwords t...
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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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04/30/2020
Investigating Transferability in Pretrained Language Models
While probing is a common technique for identifying knowledge in the rep...
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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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11/06/2019
Shaping Visual Representations with Language for Few-shot Classification
Language is designed to convey useful information about the world, thus ...
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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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03/13/2019
Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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03/13/2019
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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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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02/25/2019
Lost in Machine Translation: A Method to Reduce Meaning Loss
A desideratum of high-quality translation systems is that they preserve ...
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02/08/2019
Tensor Variable Elimination for Plated Factor Graphs
A wide class of machine learning algorithms can be reduced to variable e...
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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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11/08/2018
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucial...
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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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04/15/2018
Pragmatically Informative Image Captioning with Character-Level Reference
We combine a neural image captioner with a Rational Speech Acts (RSA) mo...
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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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06/13/2012
Church: a language for generative models
We introduce Church, a universal language for describing stochastic gene...
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05/09/2012
