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Found 11 papers, 7 papers with code
Question Generation for Adaptive Education
Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students.
LILA: Language-Informed Latent Actions
We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration.
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
Assistive Teaching of Motor Control Tasks to Humans
In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft.
Generating Language Corrections for Teaching Physical Control Tasks
AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are still quite limited.
Fairness Without Demographics in Repeated Loss Minimization
Machine learning models (e. g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e. g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss.
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance
Because training artificial neural networks from scratch is similar to showing novel objects to humans, we seek to understand the factors influencing the tolerance of CNNs to spatial transformations.
Robustness to Spurious Correlations via Human Annotations
The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.
An Empirical Analysis of Backward Compatibility in Machine Learning Systems
In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance.
Machine learning for modular multiplication
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model.
Optimistic Verifiable Training by Controlling Hardware Nondeterminism
The increasing compute demands of AI systems has led to the emergence of services that train models on behalf of clients lacking necessary resources.
