Kilian Q. Weinberger

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ASAPP INC ∙ cornell university ∙ Washington University in St Louis

Associate Professor of Machine Learning at Cornell University since 2015, Assistant Professor at Washington University in St. Louis since 2010, Research Scientist at YAHOO from 2007-2010, Research Internship at IBM from 2006-2007.

Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection

Self-driving cars must detect other vehicles and pedestrians in 3D to pl...
03/26/2021 ∙ by Yurong You, et al. ∙ 0 ∙ share

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Low-Precision Reinforcement Learning

Low-precision training has become a popular approach to reduce computati...
02/26/2021 ∙ by Johan Bjorck, et al. ∙ 51 ∙ share

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Making Paper Reviewing Robust to Bid Manipulation Attacks

Most computer science conferences rely on paper bidding to assign review...
02/09/2021 ∙ by Ruihan Wu, et al. ∙ 0 ∙ share

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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...
11/06/2020 ∙ by Cole Miles, et al. ∙ 0 ∙ share

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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...
07/24/2020 ∙ by Luyu Yang, et al. ∙ 13 ∙ share

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Wasserstein Distances for Stereo Disparity Estimation

Existing approaches to depth or disparity estimation output a distributi...
07/06/2020 ∙ by Divyansh Garg, et al. ∙ 0 ∙ share

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Revisiting Few-sample BERT Fine-tuning

We study the problem of few-sample fine-tuning of BERT contextual repres...
06/10/2020 ∙ by Tianyi Zhang, et al. ∙ 0 ∙ share

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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...
05/17/2020 ∙ by Yan Wang, et al. ∙ 18 ∙ share

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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...
04/07/2020 ∙ by Rui Qian, et al. ∙ 6 ∙ share

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On Feature Normalization and Data Augmentation

Modern neural network training relies heavily on data augmentation for i...
02/25/2020 ∙ by Boyi Li, et al. ∙ 19 ∙ share

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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...
02/24/2020 ∙ by Chuan Guo, et al. ∙ 0 ∙ share

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Revisiting Meta-Learning as Supervised Learning

Recent years have witnessed an abundance of new publications and approac...
02/03/2020 ∙ by Wei-Lun Chao, et al. ∙ 0 ∙ share

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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...
01/28/2020 ∙ by Geoff Pleiss, et al. ∙ 26 ∙ share

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Convolutional Networks with Dense Connectivity

Recent work has shown that convolutional networks can be substantially d...
01/08/2020 ∙ by Gao Huang, et al. ∙ 24 ∙ share

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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...
11/12/2019 ∙ by Yan Wang, et al. ∙ 0 ∙ share

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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...
10/30/2019 ∙ by Brian H. Wang, et al. ∙ 11 ∙ share

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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...
10/16/2019 ∙ by Tao Yu, et al. ∙ 0 ∙ share

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Integrated Triaging for Fast Reading Comprehension

Although according to several benchmarks automatic machine reading compr...
09/28/2019 ∙ by Felix Wu, et al. ∙ 0 ∙ share

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Positional Normalization

A widely deployed method for reducing the training time of deep neural n...
07/09/2019 ∙ by Boyi Li, et al. ∙ 3 ∙ share

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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...
06/14/2019 ∙ by Yurong You, et al. ∙ 20 ∙ share

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Simple Black-box Adversarial Attacks

We propose an intriguingly simple method for the construction of adversa...
05/17/2019 ∙ by Chuan Guo, et al. ∙ 7 ∙ share

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BERTScore: Evaluating Text Generation with BERT

We propose BERTScore, an automatic evaluation metric for text generation...
04/21/2019 ∙ by Tianyi Zhang, et al. ∙ 0 ∙ share

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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...
04/16/2019 ∙ by Xiaosen Wang, et al. ∙ 38 ∙ share

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Exact Gaussian Processes on a Million Data Points

Gaussian processes (GPs) are flexible models with state-of-the-art perfo...
03/19/2019 ∙ by Ke Alexander Wang, et al. ∙ 32 ∙ share

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FastFusionNet: New State-of-the-Art for DAWNBench SQuAD

In this technical report, we introduce FastFusionNet, an efficient varia...
02/28/2019 ∙ by Felix Wu, et al. ∙ 0 ∙ share

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Simplifying Graph Convolutional Networks

Graph Convolutional Networks (GCNs) and their variants have experienced ...
02/19/2019 ∙ by Felix Wu, et al. ∙ 0 ∙ share

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Gradient Regularized Budgeted Boosting

As machine learning transitions increasingly towards real world applicat...
01/13/2019 ∙ by Zhixiang Eddie Xu, et al. ∙ 0 ∙ share

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Gradient Boosted Feature Selection

A feature selection algorithm should ideally satisfy four conditions: re...
01/13/2019 ∙ by Zhixiang Eddie Xu, et al. ∙ 0 ∙ share

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Anytime Stereo Image Depth Estimation on Mobile Devices

Many real-world applications of stereo depth estimation in robotics requ...
10/26/2018 ∙ by Yan Wang, et al. ∙ 4 ∙ share

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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...
10/19/2018 ∙ by Brian H. Wang, et al. ∙ 0 ∙ share

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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...
09/28/2018 ∙ by Jacob R. Gardner, et al. ∙ 4 ∙ share

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Low Frequency Adversarial Perturbation

Recently, machine learning security has received significant attention. ...
09/24/2018 ∙ by Chuan Guo, et al. ∙ 10 ∙ share

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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...
05/22/2018 ∙ by Yan Wang, et al. ∙ 0 ∙ share

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Constant-Time Predictive Distributions for Gaussian Processes

One of the most compelling features of Gaussian process (GP) regression ...
03/16/2018 ∙ by Geoff Pleiss, et al. ∙ 0 ∙ share

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Product Kernel Interpolation for Scalable Gaussian Processes

Recent work shows that inference for Gaussian processes can be performed...
02/24/2018 ∙ by Jacob R. Gardner, et al. ∙ 0 ∙ share

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CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Deep neural networks are increasingly used on mobile devices, where comp...
11/25/2017 ∙ by Gao Huang, et al. ∙ 0 ∙ share

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On Fairness and Calibration

The machine learning community has become increasingly concerned with th...
09/06/2017 ∙ by Geoff Pleiss, et al. ∙ 0 ∙ share

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Memory-Efficient Implementation of DenseNets

The DenseNet architecture is highly computationally efficient as a resul...
07/21/2017 ∙ by Geoff Pleiss, et al. ∙ 0 ∙ share

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Densely Connected Convolutional Networks

Recent work has shown that convolutional networks can be substantially d...
08/25/2016 ∙ by Gao Huang, et al. ∙ 0 ∙ share

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Private Causal Inference

Causal inference deals with identifying which random variables "cause" o...
12/17/2015 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ share

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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,...
11/19/2015 ∙ by Jacob R. Gardner, et al. ∙ 0 ∙ share

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Compressing Convolutional Neural Networks

Convolutional neural networks (CNN) are increasingly used in many areas ...
06/14/2015 ∙ by Wenlin Chen, et al. ∙ 0 ∙ share

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Compressing Neural Networks with the Hashing Trick

As deep nets are increasingly used in applications suited for mobile dev...
04/19/2015 ∙ by Wenlin Chen, et al. ∙ 0 ∙ share

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Differentially Private Bayesian Optimization

Bayesian optimization is a powerful tool for fine-tuning the hyper-param...
01/16/2015 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ share

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Image Data Compression for Covariance and Histogram Descriptors

Covariance and histogram image descriptors provide an effective way to c...
12/04/2014 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ share

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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...
01/28/2013 ∙ by Zhixiang, et al. ∙ 0 ∙ share

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Cost-Sensitive Tree of Classifiers

Recently, machine learning algorithms have successfully entered large-sc...
10/09/2012 ∙ by Zhixiang Xu, et al. ∙ 0 ∙ share

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Distance Metric Learning for Kernel Machines

Recent work in metric learning has significantly improved the state-of-t...
08/16/2012 ∙ by Zhixiang Xu, et al. ∙ 0 ∙ share

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Rapid Feature Learning with Stacked Linear Denoisers

We investigate unsupervised pre-training of deep architectures as featur...
05/05/2011 ∙ by Zhixiang Eddie Xu, et al. ∙ 0 ∙ share

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Kilian Q. Weinberger

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