no code implementations • ICML 2020 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks.
no code implementations • 2 Apr 2024 • Anass Bairouk, Mirjana Maras, Simon Herlin, Alexander Amini, Marc Blanchon, Ramin Hasani, Patrick Chareyre, Daniela Rus
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature.
no code implementations • 2 Apr 2024 • Yao Du, Carlos M. Mateo, Mirjana Maras, Tsun-Hsuan Wang, Marc Blanchon, Alexander Amini, Daniela Rus, Omar Tahri
Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation through images.
no code implementations • 26 Nov 2023 • Qi Yang, Shreya Ravikumar, Fynn Schmitt-Ulms, Satvik Lolla, Ege Demir, Iaroslav Elistratov, Alex Lavaee, Sadhana Lolla, Elaheh Ahmadi, Daniela Rus, Alexander Amini, Alejandro Perez
We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction.
no code implementations • 26 Oct 2023 • Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
no code implementations • 1 Aug 2023 • Sadhana Lolla, Iaroslav Elistratov, Alejandro Perez, Elaheh Ahmadi, Daniela Rus, Alexander Amini
We validate capsa by implementing state-of-the-art uncertainty estimation algorithms within the capsa framework and benchmarking them on complex perception datasets.
no code implementations • 5 Apr 2023 • Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
Modern end-to-end learning systems can learn to explicitly infer control from perception.
1 code implementation • 2 Feb 2023 • Noel Loo, Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus
We show that both theoretically and empirically, reconstructed images tend to "outliers" in the dataset, and that these reconstruction attacks can be used for \textit{dataset distillation}, that is, we can retrain on reconstructed images and obtain high predictive accuracy.
1 code implementation • 21 Oct 2022 • Noel Loo, Ramin Hasani, Alexander Amini, Daniela Rus
In this limit, the kernel is frozen, and the underlying feature map is fixed.
2 code implementations • 21 Oct 2022 • Noel Loo, Ramin Hasani, Alexander Amini, Daniela Rus
Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing the entire dataset.
no code implementations • 9 Oct 2022 • Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A. Henzinger, Daniela Rus
Our results imply that the causality gap can be solved in situation one with our proposed training guideline with any modern network architecture, whereas achieving out-of-distribution generalization (situation two) requires further investigations, for instance, on data diversity rather than the model architecture.
1 code implementation • 26 Sep 2022 • Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks.
Ranked #1 on SpO2 estimation on BIDMC
1 code implementation • 26 May 2022 • Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela Rus, Song Han
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Ranked #4 on 3D Object Detection on nuScenes
no code implementations • 15 Apr 2022 • Mathias Lechner, Alexander Amini, Daniela Rus, Thomas A. Henzinger
However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance.
no code implementations • 4 Mar 2022 • Wei Xiao, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
They are interpretable at scale, achieve great test performance under limited training data, and are safety guaranteed in a series of autonomous driving scenarios such as lane keeping and obstacle avoidance.
no code implementations • 23 Nov 2021 • Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Data-driven simulators promise high data-efficiency for driving policy learning.
no code implementations • 23 Nov 2021 • Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, Daniela Rus
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
1 code implementation • 25 Jun 2021 • Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
To this end, we compute a tightly-bounded approximation of the solution of an integral appearing in LTCs' dynamics, that has had no known closed-form solution so far.
Ranked #36 on Sentiment Analysis on IMDb
1 code implementation • NeurIPS 2021 • Lucas Liebenwein, Ramin Hasani, Alexander Amini, Daniela Rus
Our empirical results suggest that pruning improves generalization for neural ODEs in generative modeling.
1 code implementation • NeurIPS 2021 • Charles Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus
We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments.
no code implementations • 20 May 2021 • Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus
On the other hand, increasing the robustness of these systems is also critical; however, even estimating the model's uncertainty is very challenging due to the cost of sampling-based methods.
1 code implementation • 13 Oct 2020 • Mathias Lechner, Ramin Hasani, Alexander Amini, Thomas A. Henzinger, Daniela Rus & Radu Grosu
A central goal of artificial intelligence in high-stakes decision-making applications is to design a single algorithm that simultaneously expresses generalizability by learning coherent representations of their world and interpretable explanations of its dynamics.
4 code implementations • 8 Jun 2020 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
We introduce a new class of time-continuous recurrent neural network models.
1 code implementation • ICLR 2020 • Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
4 code implementations • NeurIPS 2020 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
no code implementations • 25 Sep 2019 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
no code implementations • 25 Nov 2018 • Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus
We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map.
no code implementations • 1 Nov 2018 • Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
In this paper, we introduce the notion of liquid time-constant (LTC) recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with varying neuronal time-constant realized by their nonlinear synaptic transmission model.
1 code implementation • 11 Sep 2018 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as the model of biological neural circuits reparameterized for the control of an alternative task.
no code implementations • 11 Sep 2018 • Ramin M. Hasani, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, Daniela Rus
In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level.
no code implementations • 13 May 2018 • Alexander Amini, Ava Soleimany, Sertac Karaman, Daniela Rus
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.
1 code implementation • 28 Dec 2016 • Alexander Amini, Berthold Horn, Alan Edelman
Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second.