1 citations · 1 across the 7 of their papers we have counts for
8 papers
AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
Haoran Zhu, Wancong Zhang, Yann LeCun +1
Autonomous driving requires \textit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first systematically evaluate exist…
Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations
Fatemeh Naeinian, Ali Hamza, Haoran Zhu +1
End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities r…
Self-Supervised JEPA-based World Models for LiDAR Occupancy Completion and Forecasting
Haoran Zhu, Anna Choromanska
Autonomous driving, as an agent operating in the physical world, requires the fundamental capability to build \textit{world models} that capture how the environment evolves spatiot…
Streamlining Industrial Contract Management with Retrieval-Augmented LLMs
Kristi Topollai, Tolga Dimlioglu, Anna Choromanska +2
Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to pro…
Task-Level Contrastiveness for Cross-Domain Few-Shot Learning
Kristi Topollai, Anna Choromanska
Few-shot classification and meta-learning methods typically struggle to generalize across diverse domains, as most approaches focus on a single dataset, failing to transfer knowled…
OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai +1
Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language mod…
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai, Anna Choromanska
The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's memory by determining how much ea…
Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection
Haoran Zhu, Zhenyuan Dong, Kristi Topollai +2
Recently, self-supervised representation learning relying on vast amounts of unlabeled data has been explored as a pre-training method for autonomous driving. However, directly app…