Search Results for author:
Found 16 papers, 4 papers with code
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding
We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data.
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading
In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations.
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models
In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence.
Detection of ADHD based on Eye Movements during Natural Viewing
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose.
Discriminative Viewer Identification using Generative Models of Eye Gaze
We study the problem of identifying viewers of arbitrary images based on their eye gaze.
Joint Detection of Malicious Domains and Infected Clients
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable.
Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye
We study involuntary micro-movements of the eye for biometric identification.
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
We study whether a Fisher-SVM with this Fisher kernel and several reference methods are able to identify readers and estimate their level of text comprehension based on eye-tracking data.
Varying-coefficient models with isotropic Gaussian process priors
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables.
Active Comparison of Prediction Models
We address the problem of comparing the risks of two given predictive models - for instance, a baseline model and a challenger - as confidently as possible on a fixed labeling budget.
Active Estimation of F-Measures
We address the problem of estimating the F-measure of a given model as accurately as possible on a fixed labeling budget.
Throttling Poisson Processes
We study a setting in which Poisson processes generate sequences of decision-making events.
Localizing Bugs in Program Executions with Graphical Models
We devise a graphical model that supports the process of debugging software by guiding developers to code that is likely to contain defects.
Nash Equilibria of Static Prediction Games
The standard assumption of identically distributed training and test data can be violated when an adversary can exercise some control over the generation of the test data.
Transfer Learning by Distribution Matching for Targeted Advertising
We address the problem of learning classifiers for several related tasks that may differ in their joint distribution of input and output variables.
