activity
20192022
most citedInterpretable Self-Aware Neural Networks for Robust Trajectory Prediction

4 citations · 4 across the 3 of their papers we have counts for

collaborators

7 papers

cs.RO20224 cited

Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

Masha Itkina, Mykel J. Kochenderfer

Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) d…

cs.RO2022

How Do We Fail? Stress Testing Perception in Autonomous Vehicles

Harrison Delecki, Masha Itkina, Bernard Lange +2

Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse we…

cs.LG2021

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Phil Chen, Masha Itkina, Ransalu Senanayake +1

Many applications of generative models rely on the marginalization of their high-dimensional output probability distributions. Normalization functions that yield sparse probability…

cs.CV2020

Out-of-Distribution Detection for Automotive Perception

Julia Nitsch, Masha Itkina, Ransalu Senanayake +5

Neural networks (NNs) are widely used for object classification in autonomous driving. However, NNs can fail on input data not well represented by the training dataset, known as ou…

cs.CV2020

Attention Augmented ConvLSTM for Environment Prediction

Bernard Lange, Masha Itkina, Mykel J. Kochenderfer

Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techn…

cs.LG2020

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Masha Itkina, Boris Ivanovic, Ransalu Senanayake +2

Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding,…