Search Results for author:
Found 20 papers, 2 papers with code
Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound
Guidelines for practitioners were synthesized based on the results, such as the merit of IVPP with task-specific hyperparameters, and the improved performance of contrastive methods for ultrasound compared to non-contrastive counterparts.
Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks
In this study, we investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in B-mode lung ultrasound analysis.
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data.
Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound
This study investigates the utility of self-supervised pretraining prior to conducting supervised fine-tuning for the downstream task of lung sliding classification in M-mode lung ultrasound images.
A Novel Approach to Fairness in Automated Decision-Making using Affective Normalization
Any decision, such as one about who to hire, involves two components.
Dream to Explore: Adaptive Simulations for Autonomous Systems
One's ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize across experiences.
Trust-ya: design of a multiplayer game for the study of small group processes
This paper introduces the game, relates it to status theory in social psychology, and shows some simple simulated and human experiments that demonstrate how the game can be used to study status processes and dynamics in human groups.
The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning
Machine learning has the potential to aid in mitigating the human effects of climate change.
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory
State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation.
ALOHA: Artificial Learning of Human Attributes for Dialogue Agents
We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters.
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange.
"Conservatives Overfit, Liberals Underfit": The Social-Psychological Control of Affect and Uncertainty
Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together.
Improving Humanness of Virtual Agents and Users' Cooperation through Emotions
In this paper, we analyze the performance of an agent developed according to a well-accepted appraisal theory of human emotion with respect to how it modulates play in the context of a social dilemma.
Affective Neural Response Generation
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.
Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations
In this paper, we propose an extension to graph-based sentiment lexicon induction methods by incorporating distributed and semantic word representations in building the similarity graph to expand a three-dimensional sentiment lexicon.
Review of Fall Detection Techniques: A Data Availability Perspective
In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data.
Detecting Falls with X-Factor Hidden Markov Models
This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL.
A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare
This paper considers a novel approach to scalable multiagent resource allocation in dynamic settings.
Affect Control Processes: Intelligent Affective Interaction using a Partially Observable Markov Decision Process
In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents.
