2 citations · 10 across the 12 of their papers we have counts for
15 papers
When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks
Berk Tinaz, Changzhi Xie, Mahdi Soltanolkotabi
In this paper, we study the gradient descent dynamics for jointly training both layers of a one-hidden-layer ReLU network to fit a linear target function. Concretely, we consider a…
MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI
Paula Arguello, Berk Tinaz, Mohammad Shahab Sepehri +2
Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven largely by public datas…
ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models
Mohammad Shahab Sepehri, Asal Mehradfar, Berk Tinaz +2
Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To addres…
Asymmetric Prompt Weighting for Reinforcement Learning with Verifiable Rewards
Reinhard Heckel, Mahdi Soltanolkotabi, Christos Thramboulidis
Reinforcement learning with verifiable rewards has driven recent advances in LLM post-training, in particular for reasoning. Policy optimization algorithms generate a number of res…
Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning
Filip Kovačević, Hong Chang Ji, Denny Wu +2
It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning. While this phenomenon has been extensively studied in li…
ConceptMix++: Leveling the Playing Field in Text-to-Image Benchmarking via Iterative Prompt Optimization
Haosheng Gan, Berk Tinaz, Mohammad Shahab Sepehri +2
Current text-to-image (T2I) benchmarks evaluate models on rigid prompts, potentially underestimating true generative capabilities due to prompt sensitivity and creating biases that…
Hyperphantasia: A Benchmark for Evaluating the Mental Visualization Capabilities of Multimodal LLMs
Mohammad Shahab Sepehri, Berk Tinaz, Zalan Fabian +1
Mental visualization, the ability to construct and manipulate visual representations internally, is a core component of human cognition and plays a vital role in tasks involving re…
A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction
Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato +4
The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demon…
The Rich and the Simple: On the Implicit Bias of Adam and SGD
Bhavya Vasudeva, Jung Whan Lee, Vatsal Sharan +1
Adam is the de facto optimization algorithm for several deep learning applications, but an understanding of its implicit bias and how it differs from other algorithms, particularly…
FoNE: Precise Single-Token Number Embeddings via Fourier Features
Tianyi Zhou, Deqing Fu, Mahdi Soltanolkotabi +2
Large Language Models (LLMs) typically represent numbers using multiple tokens, which requires the model to aggregate these tokens to interpret numerical values. This fragmentation…
CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction
Mohammad Shahab Sepehri, Asal Mehradfar, Mahdi Soltanolkotabi +1
Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as…
MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models
Mohammad Shahab Sepehri, Zalan Fabian, Maryam Soltanolkotabi +1
Multimodal Large Language Models (MLLMs) have tremendous potential to improve the accuracy, availability, and cost-effectiveness of healthcare by providing automated solutions or s…
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning
Amin Banayeeanzade, Mahdi Soltanolkotabi, Mohammad Rostami
Multi-task learning (MTL) is a machine learning paradigm that aims to improve the generalization performance of a model on multiple related tasks by training it simultaneously on t…
Learning A Disentangling Representation For PU Learning
Omar Zamzam, Haleh Akrami, Mahdi Soltanolkotabi +1
In this paper, we address the problem of learning a binary (positive vs. negative) classifier given Positive and Unlabeled data commonly referred to as PU learning. Although rudime…
Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction
Sara Fridovich-Keil, Fabrizio Valdivia, Gordon Wetzstein +2
In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-…