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Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Self-driving cars must detect other vehicles and pedestrians in 3D to pl...
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Low-Precision Reinforcement Learning
Low-precision training has become a popular approach to reduce computati...
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Making Paper Reviewing Robust to Bid Manipulation Attacks
Most computer science conferences rely on paper bidding to assign review...
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Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing da...
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MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to adapt models from a lab...
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Wasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distributi...
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Revisiting Few-sample BERT Fine-tuning
We study the problem of few-sample fine-tuning of BERT contextual repres...
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Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In the domain of autonomous driving, deep learning has substantially imp...
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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autono...
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On Feature Normalization and Data Augmentation
Modern neural network training relies heavily on data augmentation for i...
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TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks
The complexity of large-scale neural networks can lead to poor understan...
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Revisiting Meta-Learning as Supervised Learning
Recent years have witnessed an abundance of new publications and approac...
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Identifying Mislabeled Data using the Area Under the Margin Ranking
Not all data in a typical training set help with generalization; some sa...
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Convolutional Networks with Dense Connectivity
Recent work has shown that convolutional networks can be substantially d...
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SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-shot learners aim to recognize new object classes based on a small n...
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LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
Object segmentation in three-dimensional (3-D) point clouds is a critica...
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A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Natural images are virtually surrounded by low-density misclassified reg...
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Integrated Triaging for Fast Reading Comprehension
Although according to several benchmarks automatic machine reading compr...
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Positional Normalization
A widely deployed method for reducing the training time of deep neural n...
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Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensa...
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Simple Black-box Adversarial Attacks
We propose an intriguingly simple method for the construction of adversa...
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BERTScore: Evaluating Text Generation with BERT
We propose BERTScore, an automatic evaluation metric for text generation...
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AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
Recent studies have discovered the vulnerability of Deep Neural Networks...
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Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with state-of-the-art perfo...
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FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient varia...
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Simplifying Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and their variants have experienced ...
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Gradient Regularized Budgeted Boosting
As machine learning transitions increasingly towards real world applicat...
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Gradient Boosted Feature Selection
A feature selection algorithm should ideally satisfy four conditions: re...
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Anytime Stereo Image Depth Estimation on Mobile Devices
Many real-world applications of stereo depth estimation in robotics requ...
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Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
We present a data association method for vision-based multiple pedestria...
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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
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Low Frequency Adversarial Perturbation
Recently, machine learning security has received significant attention. ...
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Resource Aware Person Re-identification across Multiple Resolutions
Not all people are equally easy to identify: color statistics might be e...
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Constant-Time Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
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Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
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CondenseNet: An Efficient DenseNet using Learned Group Convolutions
Deep neural networks are increasingly used on mobile devices, where comp...
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On Fairness and Calibration
The machine learning community has become increasingly concerned with th...
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Memory-Efficient Implementation of DenseNets
The DenseNet architecture is highly computationally efficient as a resul...
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Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially d...
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Private Causal Inference
Causal inference deals with identifying which random variables "cause" o...
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Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem,...
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Compressing Convolutional Neural Networks
Convolutional neural networks (CNN) are increasingly used in many areas ...
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Compressing Neural Networks with the Hashing Trick
As deep nets are increasingly used in applications suited for mobile dev...
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Differentially Private Bayesian Optimization
Bayesian optimization is a powerful tool for fine-tuning the hyper-param...
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Image Data Compression for Covariance and Histogram Descriptors
Covariance and histogram image descriptors provide an effective way to c...
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An alternative text representation to TF-IDF and Bag-of-Words
In text mining, information retrieval, and machine learning, text docume...
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Cost-Sensitive Tree of Classifiers
Recently, machine learning algorithms have successfully entered large-sc...
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Distance Metric Learning for Kernel Machines
Recent work in metric learning has significantly improved the state-of-t...
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Rapid Feature Learning with Stacked Linear Denoisers
We investigate unsupervised pre-training of deep architectures as featur...
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