223 citations · 475 across the 51 of their papers we have counts for
54 papers
Riccati State Space Models: Non-iterative Parallelization for Nonlinear Sequence Modeling
Mónika Farsang, Ramin Hasani, Daniela Rus +1
State space models (SSMs) achieve efficient sequence processing because their affine state updates are closed under composition and can therefore be evaluated with an associative p…
Reshape and Recur: Improving SSMs with Input Reshaping and Depth Recurrence
Mónika Farsang, Ramin Hasani, Daniela Rus +1
State Space Models (SSMs) are increasingly deployed in the Edge because they offer, at comparable performance, a smaller memory/training/inference footprint, compared to Large Lang…
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks…
Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Julian Lemmel, Felix Resch, Mónika Farsang +3
We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a memory-efficient algorithm that update…
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models
Haoyang Wu, Tsun-Hsuan Wang, Mathias Lechner +7
Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis. Recent…
Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling
Mónika Farsang, Ramin Hasani, Daniela Rus +1
We present LrcSSM, a recurrent model that processes long sequences as fast as today's linear state-space layers. By forcing its Jacobian matrix to be diagonal…
Enhancing Steering Estimation with Semantic-Aware GNNs
Fouad Makiyeh, Huy-Dung Nguyen, Patrick Chareyre +3
Steering estimation is a critical task in autonomous driving, traditionally relying on 2D image-based models. In this work, we explore the advantages of incorporating 3D spatial in…
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering
Fouad Makiyeh, Mark Bastourous, Anass Bairouk +7
Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end meth…
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference
Huy-Dung Nguyen, Anass Bairouk, Mirjana Maras +6
Autonomous driving systems require a comprehensive understanding of the environment, achieved by extracting visual features essential for perception, planning, and control. However…
State-Free Inference of State-Space Models: The Transfer Function Approach
Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro +10
We approach designing a state-space model for deep learning applications through its dual representation, the transfer function, and uncover a highly efficient sequence parallel in…
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks
Alex Quach, Makram Chahine, Alexander Amini +2
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavio…
Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder
Anass Bairouk, Mirjana Maras, Simon Herlin +5
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of mo…
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
Mónika Farsang, Mathias Lechner, David Lung +3
Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems. Bio-electrical synapses directly…
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Neehal Tumma, Mathias Lechner, Noel Loo +2
Developing autonomous agents that can interact with changing environments is an open challenge in machine learning. Robustness is particularly important in these settings as agents…
On the Size and Approximation Error of Distilled Sets
Alaa Maalouf, Murad Tukan, Noel Loo +3
Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite s…
Learning Stability Attention in Vision-based End-to-end Driving Policies
Tsun-Hsuan Wang, Wei Xiao, Makram Chahine +3
Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since the…
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Noam Buckman, Shiva Sreeram, Mathias Lechner +4
Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersect…
Dataset Distillation with Convexified Implicit Gradients
Noel Loo, Ramin Hasani, Mathias Lechner +1
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this…
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Noel Loo, Ramin Hasani, Mathias Lechner +2
Modern deep learning requires large volumes of data, which could contain sensitive or private information that cannot be leaked. Recent work has shown for homogeneous neural networ…
Towards Cooperative Flight Control Using Visual-Attention
Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang +5
The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. We propose an air-guardian system that facilitates cooperation between a…
Efficient Dataset Distillation Using Random Feature Approximation
Noel Loo, Ramin Hasani, Alexander Amini +1
Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing…
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Noel Loo, Ramin Hasani, Alexander Amini +1
Two key challenges facing modern deep learning are mitigating deep networks' vulnerability to adversarial attacks and understanding deep learning's generalization capabilities. Tow…
Pruning by Active Attention Manipulation
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani +2
Filter pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-sco…