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Found 42 papers, 23 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.
State-Free Inference of State-Space Models: The Transfer Function Approach
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 inference algorithm that is state-free: unlike other proposed algorithms, state-free inference does not incur any significant memory or computational cost with an increase in state size.
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies.
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
In this work we aim to determine the impact of using chemical synapses compared to electrical synapses, in both sparse and all-to-all connected networks.
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
In this work, we explore the application of recurrent neural networks to tasks of this nature and understand how a parameterization of their recurrent connectivity influences robustness in closed-loop settings.
On the Size and Approximation Error of Distilled Sets
Despite significant empirical progress in recent years, there is little understanding of the theoretical limitations/guarantees of dataset distillation, specifically, what excess risk is achieved by distillation compared to the original dataset, and how large are distilled datasets?
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers.
Dataset Distillation with Convexified Implicit Gradients
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art.
Ranked #1 on
Dataset Distillation - 1IPC
on TinyImageNet
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.
Towards Cooperative Flight Control Using Visual-Attention
We propose an air-guardian system that facilitates cooperation between a pilot with eye tracking and a parallel end-to-end neural control system.
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
We study the problem of training and certifying adversarially robust quantized neural networks (QNNs).
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees.
Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
We show that this procedure can also be adapted to formally verifying that, under a given Lipschitz continuous control policy, the stochastic system stabilizes within some stabilizing region with probability~$1$.
On the Forward Invariance of Neural ODEs
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
PyHopper -- Hyperparameter optimization
Hyperparameter tuning is a fundamental aspect of machine learning research.
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
Entangled Residual Mappings
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers.
Learning Stabilizing Policies in Stochastic Control Systems
In this work, we address the problem of learning provably stable neural network policies for stochastic control systems.
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.
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
We consider the problem of formally verifying almost-sure (a. s.) asymptotic stability in discrete-time nonlinear stochastic control systems.
Infinite Time Horizon Safety of Bayesian Neural Networks
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.
Interactive Analysis of CNN Robustness
While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against.
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series
These models, however, face difficulties when the input data possess long-term dependencies.
GoTube: Scalable Stochastic Verification of Continuous-Depth Models
Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states.
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 #38 on
Sentiment Analysis
on IMDb
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.
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Robustness to variations in lighting conditions is a key objective for any deep vision system.
Adversarial Training is Not Ready for Robot Learning
Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations, with the cost of nominal performance drop.
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms.
On The Verification of Neural ODEs with Stochastic Guarantees
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems.
Scalable Verification of Quantized Neural Networks (Technical Report)
In this paper, we show that verifying the bit-exact implementation of quantized neural networks with bit-vector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP.
Lagrangian Reachtubes: The Next Generation
Secondly, it computes the next reachset as the intersection of two balls: one based on the Cartesian metric and the other on the new metric.
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.
Learning Long-Term Dependencies in Irregularly-Sampled Time Series
These models, however, face difficulties when the input data possess long-term dependencies.
Ranked #10 on
Sequential Image Classification
on Sequential MNIST
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network models.
Learning representations for binary-classification without backpropagation
The family of feedback alignment (FA) algorithms aims to provide a more biologically motivated alternative to backpropagation (BP), by substituting the computations that are unrealistic to be implemented in physical brains.
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.
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.
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.
Neuronal Circuit Policies
We propose an effective way to create interpretable control agents, by re-purposing the function of a biological neural circuit model, to govern simulated and real world reinforcement learning (RL) test-beds.
Worm-level Control through Search-based Reinforcement Learning
We model the tap-withdrawal (TW) neural circuit of the nematode, \textit{C. elegans}, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neural parameters as a policy for controlling the inverted pendulum problem.
