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Found 32 papers, 14 papers with code
The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
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.
Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature.
Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications
Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation through images.
Uncertainty-aware Language Modeling for Selective Question Answering
We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction.
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
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.
Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks
We validate capsa by implementing state-of-the-art uncertainty estimation algorithms within the capsa framework and benchmarking them on complex perception datasets.
Learning Stability Attention in Vision-based End-to-end Driving Policies
Modern end-to-end learning systems can learn to explicitly infer control from perception.
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
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.
Efficient Dataset Distillation Using Random Feature Approximation
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.
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
In this limit, the kernel is frozen, and the underlying feature map is fixed.
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
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.
Liquid Structural State-Space Models
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
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Ranked #4 on
3D Object Detection
on nuScenes
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance.
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
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.
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy learning.
Closed-form Continuous-time Neural Models
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 #35 on
Sentiment Analysis
on IMDb
Sparse Flows: Pruning Continuous-depth Models
Our empirical results suggest that pruning improves generalization for neural ODEs in generative modeling.
Causal Navigation by Continuous-time Neural Networks
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.
Efficient and Robust LiDAR-Based End-to-End Navigation
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.
Neural circuit policies enabling auditable autonomy
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.
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network models.
Deep Orientation Uncertainty Learning based on a Bingham Loss
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
Deep Evidential Regression
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.
Deep Evidential Uncertainty
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.
Variational End-to-End Navigation and Localization
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.
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
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.
Can a Compact Neuronal Circuit Policy be Re-purposed to Learn Simple Robotic Control?
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.
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
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.
Spatial Uncertainty Sampling for End-to-End Control
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.
Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia
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.
