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Found 35 papers, 17 papers with code
The Machine Can't Replace the Human Heart
Ultimately, by nurturing innovation and humanity together, perhaps we reach new heights of empathy previously unimaginable.
COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling
The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment.
The Topology and Geometry of Neural Representations
In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds).
Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis
The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.
Towards Healthy AI: Large Language Models Need Therapists Too
By incorporating psychotherapy and reinforcement learning techniques, the framework enables AI chatbots to learn and adapt to human preferences and values in a safe and ethical way, contributing to the development of a more human-centric and responsible AI.
Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses.
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts
We present the TherapyView, a demonstration system to help therapists visualize the dynamic contents of past treatment sessions, enabled by the state-of-the-art neural topic modeling techniques to analyze the topical tendencies of various psychiatric conditions and deep learning-based image generation engine to provide a visual summary.
A Reinforcement Learning Framework for Online Speaker Diarization
Our approach considers speaker diarization as a fully online learning problem of the speaker recognition task, where the agent receives no pretraining from any training set before deployment, and learns to detect speaker identity on the fly through reward feedbacks.
A Survey on Compositional Generalization in Applications
The field of compositional generalization is currently experiencing a renaissance in AI, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical compositional generalization problem.
Working Alliance Transformer for Psychotherapy Dialogue Classification
As a predictive measure of the treatment outcome in psychotherapy, the working alliance measures the agreement of the patient and the therapist in terms of their bond, task and goal.
Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations
Emerging research frontiers and computational advances have gradually transformed cognitive science into a multidisciplinary and data-driven field.
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing.
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time.
Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook
Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making.
Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i. e. the cell ecology.
Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling
In this work, we propose the Genetic Thompson Sampling, a bandit algorithm that keeps a population of agents and update them with genetic principles such as elite selection, crossover and mutations.
Neural Topic Modeling of Psychotherapy Sessions
In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings.
Deep Annotation of Therapeutic Working Alliance in Psychotherapy
The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment.
Geometric and Topological Inference for Deep Representations of Complex Networks
Understanding the deep representations of complex networks is an important step of building interpretable and trustworthy machine learning applications in the age of internet.
Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget
In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions.
Predicting human decision making in psychological tasks with recurrent neural networks
Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i. e., what others are thinking.
Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward
We considered a novel practical problem of online learning with episodically revealed rewards, motivated by several real-world applications, where the contexts are nonstationary over different episodes and the reward feedbacks are not always available to the decision making agents.
Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
As an important psychological and social experiment, the Iterated Prisoner's Dilemma (IPD) treats the choice to cooperate or defect as an atomic action.
Speaker Diarization as a Fully Online Learning Problem in MiniVox
We proposed a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online learning setting.
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL
Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward.
Keep It Real: a Window to Real Reality in Virtual Reality
This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world.
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry
Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing.
Split Q Learning: Reinforcement Learning with Two-Stream Rewards
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Visualizing Representational Dynamics with Multidimensional Scaling Alignment
Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM).
Regularity Normalization: Constraining Implicit Space with Minimum Description Length
Inspired by the adaptation phenomenon of biological neuronal firing, we propose regularity normalization: a reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space.
Unsupervised Attention Mechanism across Neural Network Layers
Inspired by the adaptation phenomenon of neuronal firing, we propose an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle.
Adaptive Geo-Topological Independence Criterion
We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators.
Contextual Bandit with Adaptive Feature Extraction
Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.
Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls.
