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Found 13 papers, 4 papers with code
What Matters for Active Texture Recognition With Vision-Based Tactile Sensors
By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
Speaking Multiple Languages Affects the Moral Bias of Language Models
Do the models capture moral norms from English and impose them on other languages?
Reinforcement Learning with Non-Exponential Discounting
In contrast, in economics and psychology, it has been shown that humans often adopt a hyperbolic discounting scheme, which is optimal when a specific task termination time distribution is assumed.
Improving saliency models' predictions of the next fixation with humans' intrinsic cost of gaze shifts
These maps are based on 1) a saliency map provided by an arbitrary saliency model, 2) the recently measured human cost function quantifying preferences in magnitude and direction of eye movements, and 3) a sequential exploration bonus, which changes with each subsequent gaze shift.
Inferring perceptual decision making parameters from behavior in production and reproduction tasks
Based on Bayesian decision theory we present an inference method to recover perceptual uncertainty, response variability, and the cost function underlying human responses.
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Computational level explanations based on optimal feedback control with signal-dependent noise have been able to account for a vast array of phenomena in human sensorimotor behavior.
Causal Explanations of Structural Causal Models
The question part on the user's end we believe to be solved since the user's mental model can provide the causal model.
Bayesian Classifier Fusion with an Explicit Model of Correlation
Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling.
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization
In this space we generate new true-type-fonts through active learning, render texts with the new font, and measure individual users' reading speed.
Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do
That is, we show that these norms can be captured geometrically by a direction, which can be computed, e. g., by a PCA, in the embedding space, reflecting well the agreement of phrases to social norms implicitly expressed in the training texts and providing a path for attenuating or even preventing toxic degeneration in LMs.
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems.
Catching heuristics are optimal control policies
In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty.
