19 citations · 21 across the 11 of their papers we have counts for
12 papers
SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models
Ahmadreza Jeddi, Enming Zhang, Jasper Gerigk +12
Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pruning reduces this cost, but…
AVIS: Adaptive Test-Time Scaling for Vision-Language Models
Ahmadreza Jeddi, Minh Ngoc Le, Amirhossein Kazerouni +8
Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual c…
GEAR: Genetic AutoResearch for Agentic Code Evolution
Ahmadreza Jeddi, Minh Ngoc Le, Hakki C. Karaimer +2
Autonomous research agents can already run machine learning experiments without human supervision, but many rely on a narrow search strategy: they repeatedly modify one program and…
When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains
Ahmadreza Jeddi, Kimia Shaban, Negin Baghbanzadeh +4
Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medical visual reasoning or mainly…
LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation
Ahmadreza Jeddi, Marco Ciccone, Babak Taati
Looped Transformers have emerged as an efficient and powerful class of models for reasoning in the language domain. Recent studies show that these models achieve strong performance…
PuzzleCraft: Exploration-Aware Curriculum Learning for Puzzle-Based RLVR in VLMs
Ahmadreza Jeddi, Hakki Can Karaimer, Hue Nguyen +8
RL post-training with verifiable rewards (RLVR) has become a practical route to eliciting chain-of-thought reasoning in vision--language models (VLMs), but scaling it in the visual…
Similarity-Aware Token Pruning: Your VLM but Faster
Ahmadreza Jeddi, Negin Baghbanzadeh, Elham Dolatabadi +1
The computational demands of Vision Transformers (ViTs) and Vision-Language Models (VLMs) remain a significant challenge due to the quadratic complexity of self-attention. While to…
QGen: On the Ability to Generalize in Quantization Aware Training
MohammadHossein AskariHemmat, Ahmadreza Jeddi, Reyhane Askari Hemmat +6
Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the gener…
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning
Ahmadreza Jeddi, Mohammad Javad Shafiee, Alexander Wong
Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for improving the robustness of the deep neural networks. However, PGD AT has been shown to…
Tackling the Problem of Limited Data and Annotations in Semantic Segmentation
Ahmadreza Jeddi
In this work, the case of semantic segmentation on a small image dataset (simulated by 1000 randomly selected images from PASCAL VOC 2012), where only weak supervision signals (scr…
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi +2
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg +2
While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widesprea…