Search Results for author: Baihan Lin

Found 36 papers, 17 papers with code

Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models

no code implementations8 May 2024 Aylin Gunal, Baihan Lin, Djallel Bouneffouf

Given the increasing demand for mental health assistance, artificial intelligence (AI), particularly large language models (LLMs), may be valuable for integration into automated clinical support systems.

Language Modelling Large Language Model +1

The Machine Can't Replace the Human Heart

no code implementations29 Feb 2024 Baihan Lin

Ultimately, by nurturing innovation and humanity together, perhaps we reach new heights of empathy previously unimaginable.

The Topology and Geometry of Neural Representations

1 code implementation20 Sep 2023 Baihan Lin, Nikolaus Kriegeskorte

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).

Model Selection Specificity

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

no code implementations6 Jun 2023 Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori

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

no code implementations2 Apr 2023 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Kush R. Varshney

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.

Chatbot

Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics

no code implementations16 Mar 2023 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses.

reinforcement-learning Reinforcement Learning (RL)

A Reinforcement Learning Framework for Online Speaker Diarization

no code implementations21 Feb 2023 Baihan Lin, Xinxin Zhang

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.

Decision Making Domain Adaptation +5

TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts

no code implementations21 Feb 2023 Baihan Lin, Stefan Zecevic, Djallel Bouneffouf, Guillermo Cecchi

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.

Image Generation Time Series +1

A Survey on Compositional Generalization in Applications

no code implementations2 Feb 2023 Baihan Lin, Djallel Bouneffouf, Irina Rish

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

1 code implementation27 Oct 2022 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

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.

Classification

Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

no code implementations24 Oct 2022 Baihan Lin

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.

Recommendation Systems reinforcement-learning +1

Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations

no code implementations24 Oct 2022 Baihan Lin

Emerging research frontiers and computational advances have gradually transformed cognitive science into a multidisciplinary and data-driven field.

Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook

no code implementations15 Jun 2022 Baihan Lin

Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making.

Decision Making Management

Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

1 code implementation29 Apr 2022 Baihan Lin

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.

Time Series Time Series Analysis +1

Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling

1 code implementation26 Apr 2022 Baihan Lin

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.

Decision Making Evolutionary Algorithms +2

Neural Topic Modeling of Psychotherapy Sessions

no code implementations13 Apr 2022 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Ravi Tejwani

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.

Time Series Time Series Analysis

Deep Annotation of Therapeutic Working Alliance in Psychotherapy

no code implementations12 Apr 2022 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

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

no code implementations10 Mar 2022 Baihan Lin

Understanding the deep representations of complex networks is an important step of building interpretable and trustworthy machine learning applications in the age of internet.

Model Selection Specificity +1

Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget

1 code implementation30 Jun 2021 Baihan Lin, Djallel Bouneffouf

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

1 code implementation22 Oct 2020 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

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.

Decision Making Time Series +1

Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward

1 code implementation17 Sep 2020 Baihan Lin

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.

Clustering Decision Making +3

Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior

1 code implementation9 Jun 2020 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

As an important psychological and social experiment, the Iterated Prisoner's Dilemma (IPD) treats the choice to cooperate or defect as an atomic action.

Multi-Armed Bandits reinforcement-learning +1

Speaker Diarization as a Fully Online Learning Problem in MiniVox

1 code implementation8 Jun 2020 Baihan Lin, Xinxin Zhang

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.

Self-Supervised Learning speaker-diarization +1

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

1 code implementation10 May 2020 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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.

Decision Making Multi-Armed Bandits +1

Keep It Real: a Window to Real Reality in Virtual Reality

1 code implementation IJCAI 2020 Baihan Lin

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

no code implementations NeurIPS Workshop Neuro_AI 2019 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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.

Decision Making Q-Learning +3

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

1 code implementation21 Jun 2019 Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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.

Decision Making Q-Learning +2

Split Q Learning: Reinforcement Learning with Two-Stream Rewards

1 code implementation21 Jun 2019 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

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.

Decision Making Q-Learning +4

Visualizing Representational Dynamics with Multidimensional Scaling Alignment

no code implementations21 Jun 2019 Baihan Lin, Marieke Mur, Tim Kietzmann, Nikolaus Kriegeskorte

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).

Object Categorization

Regularity Normalization: Constraining Implicit Space with Minimum Description Length

no code implementations11 Mar 2019 Baihan Lin

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.

Model Selection

Unsupervised Attention Mechanism across Neural Network Layers

1 code implementation27 Feb 2019 Baihan Lin

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.

Few-Shot Learning Image Classification +3

Adaptive Geo-Topological Independence Criterion

1 code implementation6 Oct 2018 Baihan Lin, Nikolaus Kriegeskorte

We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators.

Contextual Bandit with Adaptive Feature Extraction

1 code implementation3 Feb 2018 Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Irina Rish

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.

Clustering Decision Making +2

Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions

1 code implementation11 Nov 2017 Avinash Bukkittu, Baihan Lin, Trung Vu, Itsik Pe'er

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.

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