| Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract) | 0 | 0.34 | 2022 |
| EIFFeL: Ensuring Integrity for Federated Learning | 0 | 0.34 | 2022 |
| Data Appraisal Without Data Sharing | 0 | 0.34 | 2022 |
| Omnivore: A Single Model for Many Visual Modalities | 0 | 0.34 | 2022 |
| Bounding Training Data Reconstruction in Private (Deep) Learning. | 0 | 0.34 | 2022 |
| Revisiting Weakly Supervised Pre-Training of Visual Perception Models | 0 | 0.34 | 2022 |
| Making Paper Reviewing Robust to Bid Manipulation Attacks | 0 | 0.34 | 2021 |
| Measuring data leakage in machine-learning models with Fisher information. | 0 | 0.34 | 2021 |
| Certified Data Removal from Machine Learning Models | 0 | 0.34 | 2020 |
| Self-Supervised Learning Of Pretext-Invariant Representations | 11 | 0.56 | 2020 |
| Feature Denoising for Improving Adversarial Robustness | 43 | 0.98 | 2019 |
| Does Object Recognition Work for Everyone? | 1 | 0.35 | 2019 |
| PHYRE: A New Benchmark for Physical Reasoning | 2 | 0.36 | 2019 |
| Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding. | 1 | 0.35 | 2019 |
| Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search | 2 | 0.35 | 2019 |
| Learning by Asking Questions | 8 | 0.45 | 2018 |
| Pawe: Polysemy Aware Word Embeddings | 0 | 0.34 | 2018 |
| Multi-Scale Dense Networks for Resource Efficient Image Classification | 24 | 0.76 | 2018 |
| Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model. | 9 | 0.59 | 2018 |
| Memory-Efficient Implementation of DenseNets. | 9 | 0.53 | 2017 |
| Multi-Scale Dense Convolutional Networks for Efficient Prediction. | 6 | 0.52 | 2017 |
| Submanifold Sparse Convolutional Networks. | 16 | 0.63 | 2017 |
| Countering Adversarial Images using Input Transformations. | 77 | 1.46 | 2017 |
| Modeling Time Series Similarity with Siamese Recurrent Networks. | 4 | 0.40 | 2016 |
| Persistent Self-Supervised Learning: From Stereo To Monocular Vision For Obstacle Avoidance | 3 | 0.42 | 2016 |
| Adaptive stereo similarity fusion using confidence measures | 6 | 0.55 | 2015 |
| Automatic Thread-Level Canvas Analysis: A machine-learning approach to analyzing the canvas of paintings | 4 | 0.46 | 2015 |
| Approximated and User Steerable tSNE for Progressive Visual Analytics. | 37 | 1.11 | 2015 |
| Introduction to the special issue on visual analytics using multidimensional projections. | 1 | 0.35 | 2015 |
| Feature-Level Domain Adaptation | 7 | 0.46 | 2015 |
| Time Series Classification using the Hidden-Unit Logistic Model | 0 | 0.34 | 2015 |
| Accelerating t-SNE using tree-based algorithms | 32 | 1.13 | 2014 |
| Marginalizing Corrupted Features. | 0 | 0.34 | 2014 |
| Hybrid Kinect Depth Map Refinement for Transparent Objects | 3 | 0.42 | 2014 |
| Stereo Similarity Metric Fusion Using Stereo Confidence | 0 | 0.34 | 2014 |
| On Fast Trackers that are Robust to Partial Occlusions | 0 | 0.34 | 2014 |
| Speeding Up Tracking by Ignoring Features | 5 | 0.48 | 2014 |
| Improving Object Tracking by Adapting Detectors | 1 | 0.35 | 2014 |
| Graph-Based Kinship Recognition | 9 | 0.49 | 2014 |
| Preserving Structure in Model-Free Tracking | 60 | 1.81 | 2014 |
| Learning with Marginalized Corrupted Features. | 58 | 8.32 | 2013 |
| Divvy: fast and intuitive exploratory data analysis | 0 | 0.34 | 2013 |
| Structure Preserving Object Tracking | 46 | 1.27 | 2013 |
| Barnes-Hut-SNE | 14 | 2.72 | 2013 |
| Stochastic triplet embedding. | 17 | 0.74 | 2012 |
| Analyzing floristic inventories with multiple maps. | 0 | 0.34 | 2012 |
| Visualizing non-metric similarities in multiple maps | 28 | 1.18 | 2012 |
| Accelerating Cost Aggregation for Real-Time Stereo Matching | 4 | 0.39 | 2012 |
| A Behavioral Investigation of Dimensionality Reduction. | 9 | 0.53 | 2012 |
| Audio-visual emotion challenge 2012: a simple approach | 7 | 0.59 | 2012 |
