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Found 14 papers, 2 papers with code
Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedback
With the aim to provide teachers with more specific, frequent, and actionable feedback about their teaching, we explore how Large Language Models (LLMs) can be used to estimate ``Instructional Support'' domain scores of the CLassroom Assessment Scoring System (CLASS), a widely used observation protocol.
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification
We consider a novel clustering task in which clusters can have compositional relationships, e. g., one cluster contains images of rectangles, one contains images of circles, and a third (compositional) cluster contains images with both objects.
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification
For the task of face verification, we explore the utility of harnessing auxiliary facial emotion labels to impose explicit geometric constraints on the embedding space when training deep embedding models.
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
We propose a new method for speaker diarization that can handle overlapping speech with 2+ people.
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate
In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS observation protocol that is widely used in educational research.
Compositional Embeddings for Multi-Label One-Shot Learning
We present a compositional embedding framework that infers not just a single class per input image, but a set of classes, in the setting of one-shot learning.
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets
We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e. g., image, video, audio signal).
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
In particular: (1) We show that if the true correlation between $U$ and $V$ is $r$, then the expected sample correlation, over all vectors $\mathcal{T}^n$ whose correlation with $U$ is $q$, is $qr$.
How Does Knowledge of the AUC Constrain the Set of Possible Ground-truth Labelings?
Recent work on privacy-preserving machine learning has considered how data-mining competitions such as Kaggle could potentially be "hacked", either intentionally or inadvertently, by using information from an oracle that reports a classifier's accuracy on the test set.
Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle
In the context of data-mining competitions (e. g., Kaggle, KDDCup, ILSVRC Challenge), we show how access to an oracle that reports a contestant's log-loss score on the test set can be exploited to deduce the ground-truth of some of the test examples.
Delving Deeper into MOOC Student Dropout Prediction
In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice.
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests
In this paper we provide proofs-of-concept of how knowledge of the AUC of a set of guesses can be used, in two different kinds of attacks, to improve the accuracy of those guesses.
Understanding ACT-R - an Outsider's Perspective
The ACT-R theory of cognition developed by John Anderson and colleagues endeavors to explain how humans recall chunks of information and how they solve problems.
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise
However, using these services to label large databases brings with it new theoretical and practical challenges: (1) The labelers may have wide ranging levels of expertise which are unknown a priori, and in some cases may be adversarial; (2) images may vary in their level of difficulty; and (3) multiple labels for the same image must be combined to provide an estimate of the actual label of the image.
