Weinberger, Kilian Q.

Densely Connected Convolutional Networks

Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 48,960

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 48,566

On calibration of modern neural networks

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 3,435

BERTScore: Evaluating Text Generation with BERT

Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,865

Deep Networks with Stochastic Depth

Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 2,104

Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsAdvanced Vision and Imaging

Cited by 1,154

Compressing Neural Networks with the Hashing Trick

Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 974

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Huang, G., Liu, S., Maaten, L. V. D., & Weinberger, K. Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 904

Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

Siegel, J. S., Ramsey, L. E., Snyder, A. Z., Metcalf, N. V., Chacko, R. V., Weinberger, K., Baldassarre, A., Hacker, C. D., Shulman, G. L., & Corbetta, M. (2016) · Proceedings of the National Academy of Sciences

Functional Brain Connectivity StudiesStroke Rehabilitation and Recovery

Cited by 656

Simple Black-box Adversarial Attacks

Guo, C., Gardner, J. R., You, Y., Wilson, A. G., & Weinberger, K. Q. (2019) · arXiv

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 427

Language-driven Semantic Segmentation

Li, B., Weinberger, K. Q., Belongie, S., Koltun, V., & Ranftl, R. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 316

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Wang, Y., Chao, W.-L., Weinberger, K. Q., & van der Maaten, L. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 245

Fast flux discriminant for large-scale sparse nonlinear classification

Chen, W., Chen, Y., & Weinberger, K. Q. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Stochastic Gradient Optimization TechniquesFace and Expression Recognition

Cited by 12