RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems

Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the task of action detection. We develop a novel ranking method to rank videos based on the degree of global camera motion. For the high ranking camera videos we show that the accuracy of action detection is decreased. We propose an action detection pipeline that is robust to the camera motion effect and verify it empirically. Specifically, we do actor feature alignment across frames and couple global scene features with local actor-specific features. We do feature alignment using a novel formulation of the Spatio-temporal Sampling Network (STSN) but with multi-scale offset prediction and refinement using a pyramid structure. We also propose a novel input dependent weighted averaging strategy for fusing local and global features. We show the applicability of our network on our dataset of moving camera videos with high camera motion (MOVE dataset) with a 4.1% increase in frame mAP and 17% increase in video mAP.

Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics

In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most of the existing deep learning models for modeling spatiotemporal dynamics are either designed for data in a regular grid or struggle to uncover the spatial relations from sparse and irregularly-spaced data sites. We propose a deep learning model that learns to predict unknown spatiotemporal dynamics using data from sparsely-distributed data sites. We base our approach on Radial Basis Function (RBF) collocation method which is often used for meshfree solution of partial differential equations (PDEs). The RBF framework allows us to unravel the observed spatiotemporal function and learn the spatial interactions among data sites on the RBF-space. The learned spatial features are then used to compose multilevel transformations of the raw observations and predict its evolution in future time steps. We demonstrate the advantage of our approach using both synthetic and real-world climate data.

$μ$DARTS: Model Uncertainty-Aware Differentiable Architecture Search

We present a Model Uncertainty-aware Differentiable ARchiTecture Search ($\mu$DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We introduce concrete dropout within DARTS cells and include a Monte-Carlo regularizer within the training loss to optimize the concrete dropout probabilities. A predictive variance term is introduced in the validation loss to enable searching for architecture with minimal model uncertainty. The experiments on CIFAR10, CIFAR100, SVHN, and ImageNet verify the effectiveness of $\mu$DARTS in improving accuracy and reducing uncertainty compared to existing DARTS methods. Moreover, the final architecture obtained from $\mu$DARTS shows higher robustness to noise at the input image and model parameters compared to the architecture obtained from existing DARTS methods.

Characterization of Generalizability of Spike Time Dependent Plasticity trained Spiking Neural Networks

A Spiking Neural Network (SNN) trained with Spike Time Dependent Plasticity (STDP) is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN and characterizes the generalizability vs learnability trade-off in an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model to improve the generalizability properties of an SNN.

A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor

In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit and time overhead. We benchmark the QHAM by testing its effective memory capacity against qubit- and circuit-level errors and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.

A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection

This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on Convolutional SNN using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty error. FSHNN provides better accuracy compared to DNN based object detectors while being 150X energy-efficient. It also outperforms these object detectors, when subjected to noisy input data and less labeled training data with a lower uncertainty error.

Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation

Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine learning (ML) approach used in these HMDs fail to address the uncertain predictions, including those made on zero-day malware. The ML models used in HMDs are agnostic to the uncertainty that determines whether the model "knows what it knows," severely undermining its trustworthiness. We propose an ensemble-based approach that quantifies uncertainty in predictions made by ML models of an HMD, when it encounters an unknown workload than the ones it was trained on. We test our approach on two different HMDs that have been proposed in the literature. We show that the proposed uncertainty estimator can detect >90% of unknown workloads for the Power-management based HMD, and conclude that the overlapping benign and malware classes undermine the trustworthiness of the Performance Counter-based HMD.

A Deep Learning-based Collocation Method for Modeling Unknown PDEs from Sparse Observation

Deep learning-based modeling of dynamical systems driven by partial differential equations (PDEs) has become quite popular in recent years. However, most of the existing deep learning-based methods either assume strong physics prior, or depend on specific initial and boundary conditions, or require data in dense regular grid making them inapt for modeling unknown PDEs from sparsely-observed data. This paper presents a deep learning-based collocation method for modeling dynamical systems driven by unknown PDEs when data sites are sparsely distributed. The proposed method is spatial dimension-independent, geometrically flexible, learns from sparsely-available data and the learned model does not depend on any specific initial and boundary conditions. We demonstrate our method in the forecasting task for two-dimensional wave equation and Burgers-Fisher equation in multiple geometries with different boundary conditions.

Neural Identification for Control

We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-Link pendulum balancing, pendulum on cart balancing, and wheeled vehicle path following.