Search Results for author:
Found 30 papers, 10 papers with code
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution.
COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling
This integrated framework enhances the understanding of therapeutic interactions, enables timely feedback for therapists on the quality of therapeutic relationships, and provides clear, actionable insights to improve the effectiveness of psychotherapy.
Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description
In this paper, we focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population.
Effective Latent Differential Equation Models via Attention and Multiple Shooting
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques.
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.
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.
PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization
Chronic pain is a pervasive disorder which is often very disabling and is associated with comorbidities such as depression and anxiety.
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.
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.
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 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.
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.
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.
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.
Nearly-Unsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.
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.
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.
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.
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.
Kernelized Hashcode Representations for Relation Extraction
Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia
The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures.
Efficient Data Representation by Selecting Prototypes with Importance Weights
Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus.
The ontogeny of discourse structure mimics the development of literature
In literature, monotonic asymptotic changes over time were remarkable: While lexical diversity, long-range recurrence and graph size increased away from near-randomness, short-range recurrence declined, from above to below random levels.
Emotional Intensity analysis in Bipolar subjects
The massive availability of digital repositories of human thought opens radical novel way of studying the human mind.
