50 citations · 54 across the 3 of their papers we have counts for
7 papers
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification
Anand Ramakrishnan, Minh Pham, Jacob Whitehill
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 trainin…
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
Zeqian Li, Jacob Whitehill
We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedd…
Compositional Embeddings for Multi-Label One-Shot Learning
Zeqian Li, Michael C. Mozer, Jacob Whitehill
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. Specifically, we prop…
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill, Anand Ramakrishnan
Automatic machine learning-based detectors of various psychological and social phenomena (e.g., emotion, stress, engagement) have great potential to advance basic science. However,…
How Does Knowledge of the AUC Constrain the Set of Possible Ground-truth Labelings?
Jacob Whitehill
Recent work on privacy-preserving machine learning has considered how data-mining competitions such as Kaggle could potentially be "hacked", either intentionally or inadvertently,…
Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle
Jacob Whitehill
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…