no code implementations • 8 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.
no code implementations • 29 Feb 2024 • Baihan Lin
Ultimately, by nurturing innovation and humanity together, perhaps we reach new heights of empathy previously unimaginable.
no code implementations • 22 Feb 2024 • Baihan Lin, Djallel Bouneffouf, Yulia Landa, Rachel Jespersen, Cheryl Corcoran, Guillermo Cecchi
The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment.
1 code implementation • 20 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).
no code implementations • 6 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.
no code implementations • 2 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.
no code implementations • 16 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.
no code implementations • 21 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.
no code implementations • 21 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.
no code implementations • 2 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.
1 code implementation • 27 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.
no code implementations • 24 Oct 2022 • Baihan Lin
Emerging research frontiers and computational advances have gradually transformed cognitive science into a multidisciplinary and data-driven field.
no code implementations • 24 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.
no code implementations • 27 Aug 2022 • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time.
no code implementations • 15 Jun 2022 • Baihan Lin
Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making.
1 code implementation • 29 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.
1 code implementation • 26 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.
no code implementations • 13 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.
no code implementations • 12 Apr 2022 • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf
The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment.
no code implementations • 10 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.
1 code implementation • 30 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.
1 code implementation • 22 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.
1 code implementation • 17 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.
1 code implementation • 9 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.
1 code implementation • 8 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.
1 code implementation • 10 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.
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.
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.
1 code implementation • 21 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.
no code implementations • 21 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).
1 code implementation • 21 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.
no code implementations • 11 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.
1 code implementation • 27 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.
1 code implementation • 6 Oct 2018 • Baihan Lin, Nikolaus Kriegeskorte
We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators.
1 code implementation • 3 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.
1 code implementation • 11 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.