Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware. Seshat: A tool for managing and verifying annotation campaigns of audio data. S$^{2}$OMGAN: Shortcut from Remote Sensing Images to Online Maps. Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods. Artificial Benchmark for …

HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks. Learning word-referent mappings and concepts from raw inputs. Learning functions varying along an active subspace. Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection. Deep connections between learning from limited la …

DWM: A Decomposable Winograd Method for Convolution Acceleration. AVATAR – Machine Learning Pipeline Evaluation Using Surrogate Model. Distributed Sketching Methods for Privacy Preserving Regression. CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries. On the rate of convergence o …

Learning to Generate Multiple Style Transfer Outputs for an Input Sentence. Regularized Optimal Transport is Ground Cost Adversarial. Co-occurrence Background Model with Superpixels for Robust Background Initialization. Excitation-based Voice Quality Analysis and Modification. Boosting Algorithms for Estimating Optimal Individualized Treatment Rule …

Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization. Reasoning-Aware Graph Convolutional Network for Visual Question Answering. Attention on Abstract Visual Reasoning. CLEVRER: Collision Events for Video Representation and Reasoning. Deep End-to-end Unsupervised Anomaly Detection. Attentive Weights Generation for Few Shot Lea …

Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. Axial Attention in Multidimensional Transformers. Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling. Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients. Robust Subspace Recovery La …

Learning to Reach Goals Without Reinforcement Learning. HOW IMPORTANT ARE NETWORK WEIGHTS? TO WHAT EXTENT DO THEY NEED AN UPDATE?. Universal Adversarial Attack Using Very Few Test Examples. THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION. Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. Unre …