3 citations · 5 across the 9 of their papers we have counts for
9 papers
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
Mohammad R. Rezaei, Tejas Balaji, Rahul G. Krishnan
Irregularly sampled multivariate event streams remain a difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orde…
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
Mohammad Reza Rezaei, Reza Saadati Fard, Jayson L. Parker +2
Large Language Models (LLMs) have significantly advanced medical question-answering by leveraging extensive clinical data and medical literature. However, the rapid evolution of me…
Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling
Mohammad R. Rezaei, Milos R. Popovic, Milad Lankarany +1
Conditional Flow Matching (CFM) models can generate high-quality samples from a non-informative prior, but they can be slow, often needing hundreds of network evaluations (NFE). To…
Alternators For Sequence Modeling
Mohammad Reza Rezaei, Adji Bousso Dieng
This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (O…
Latent Dynamical Implicit Diffusion Processes
Mohammad R. Rezaei
Latent dynamical models are commonly used to learn the distribution of a latent dynamical process that represents a sequence of noisy data samples. However, producing samples from…
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models
Mohammad R. Rezaei, Reza Saadati Fard, Ebrahim Pourjafari +6
The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments…
Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
Mohammad R. Rezaei, Milos R. Popovic, Milad Lankarany +1
The state-space models (SSMs) are widely utilized in the analysis of time-series data. SSMs rely on an explicit definition of the state and observation processes. Characterizing th…
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture
Ebrahim Pourjafari, Navid Ziaei, Mohammad R. Rezaei +5
This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks. The model is designed bas…
Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning
Aman Bhargava, Mohammad R. Rezaei, Milad Lankarany
An ongoing challenge in neural information processing is: how do neurons adjust their connectivity to improve task performance over time (i.e., actualize learning)? It is widely be…