63 citations · 91 across the 10 of their papers we have counts for
12 papers
Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why
Mohammadreza Armandpour, Fatih Ilhan, David Harrison +6
On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under whi…
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh +8
Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes availab…
Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond
Mohammadreza Armandpour, Ali Sadeghian, Huangjie Zheng +2
Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the…
Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification
Renat Sergazinov, Mohammadreza Armandpour, Irina Gaynanova
Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such m…
Bayesian Graph Contrastive Learning
Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali +3
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods…
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Deep Spatio-Temporal Wind Power Forecasting
Jiangyuan Li, Mohammadreza Armandpour
Wind power forecasting has drawn increasing attention among researchers as the consumption of renewable energy grows. In this paper, we develop a deep learning approach based on en…
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks
Mohammadreza Armandpour, Brian Kidd, Yu Du +1
In this paper, we study the problem of blood glucose forecasting and provide a deep personalized solution. Predicting blood glucose level in people with diabetes has significant va…
Partition-Guided GANs
Mohammadreza Armandpour, Ali Sadeghian, Chunyuan Li +1
Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconne…
ChronoR: Rotation Based Temporal Knowledge Graph Embedding
Ali Sadeghian, Mohammadreza Armandpour, Anthony Colas +1
Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challeng…
Convex Polytope Trees
Mohammadreza Armandpour, Mingyuan Zhou
A decision tree is commonly restricted to use a single hyperplane to split the covariate space at each of its internal nodes. It often requires a large number of nodes to achieve h…
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding +1
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, mo…