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2020 – today
- 2024
[j10]Burak Aksar
, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun:
Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning. IEEE Trans. Parallel Distributed Syst. 35(4): 693-706 (2024)- [c52]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Descriptor and Word Soups Q: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning. CVPR 2024: 27005-27015 - [c51]Zuzhao Ye, Gregory Ciccarelli, Brian Kulis:
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting. ICASSP 2024: 10826-10830 - [i29]Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis:
Image-Caption Encoding for Improving Zero-Shot Generalization. CoRR abs/2402.02662 (2024) - [i28]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
A Data Centric Approach for Unsupervised Domain Generalization via Retrieval from Web Scale Multimodal Data. CoRR abs/2402.04416 (2024) - [i27]Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen:
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data. CoRR abs/2403.14048 (2024) - 2023
- [c50]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization. ICML 2023: 20906-20938 - [c49]Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun:
Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems. SC 2023: 26:1-26:14 - [i26]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning. CoRR abs/2311.13612 (2023) - 2022
- [c48]Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Ayse K. Coskun:
ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems. CLUSTER 2022: 369-380 - [c47]Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L. Geyer, Venkatesh Saligrama, Brian Kulis:
Faster Algorithms for Learning Convex Functions. ICML 2022: 20176-20194 - [c46]Christin Jose, Joe Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis:
Latency Control for Keyword Spotting. INTERSPEECH 2022: 1891-1895 - [c45]Xiao Wang, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. PCS 2022: 97-101 - [i25]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling. CoRR abs/2205.13508 (2022) - [i24]Christin Jose, Joseph Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis:
Latency Control for Keyword Spotting. CoRR abs/2206.07261 (2022) - [i23]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Supervised Metric Learning for Retrieval via Contextual Similarity Optimization. CoRR abs/2210.01908 (2022) - 2021
- [c44]Mohammad Omar Khursheed, Christin Jose, Rajath Kumar, Gengshen Fu, Brian Kulis, Santosh Kumar Cheekatmalla:
Tiny-CRNN: Streaming Wakeword Detection in a Low Footprint Setting. ASRU 2021: 541-547 - [c43]Minxu Peng, Mertcan Cokbas, Unay Dorken Gallastegi, Prakash Ishwar, Janusz Konrad, Brian Kulis, Vivek K. Goyal:
Convolutional Neural Network Denoising of Focused Ion Beam Micrographs. MLSP 2021: 1-6 - [c42]Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen:
Real-time Localized Photorealistic Video Style Transfer. WACV 2021: 1088-1097 - [i22]Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. CoRR abs/2105.07512 (2021) - [i21]Sivaramakrishnan Sankarapandian, Brian Kulis:
$β$-Annealed Variational Autoencoder for glitches. CoRR abs/2107.10667 (2021) - [i20]Mohammad Omar Khursheed, Christin Jose, Rajath Kumar, Gengshen Fu, Brian Kulis, Santosh Kumar Cheekatmalla:
Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting. CoRR abs/2109.14725 (2021) - [i19]Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis:
Faster Convex Lipschitz Regression via 2-block ADMM. CoRR abs/2111.01348 (2021) - 2020
- [c41]Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen:
Joint Bilateral Learning for Real-Time Universal Photorealistic Style Transfer. ECCV (8) 2020: 327-342 - [c40]Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis:
Deep Divergence Learning. ICML 2020: 2027-2037 - [c39]Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama:
Piecewise Linear Regression via a Difference of Convex Functions. ICML 2020: 8895-8904 - [c38]Joe Wang, Rajath Kumar, Mike Rodehorst, Brian Kulis, Shiv Naga Prasad Vitaladevuni:
An Audio-Based Wakeword-Independent Verification System. INTERSPEECH 2020: 1952-1956 - [c37]Rajath Kumar, Mike Rodehorst, Joe Wang, Jiacheng Gu, Brian Kulis:
Building a Robust Word-Level Wakeword Verification Network. INTERSPEECH 2020: 1972-1976 - [c36]Hongyi Liu, Apurva Abhyankar, Yuriy Mishchenko, Thibaud Sénéchal, Gengshen Fu, Brian Kulis, Noah D. Stein, Anish Shah, Shiv Naga Prasad Vitaladevuni:
Metadata-Aware End-to-End Keyword Spotting. INTERSPEECH 2020: 2282-2286 - [c35]Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis:
Learning to Approximate a Bregman Divergence. NeurIPS 2020 - [i18]Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen:
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer. CoRR abs/2004.10955 (2020) - [i17]Kubra Cilingir, Rachel Manzelli, Brian Kulis:
Deep Divergence Learning. CoRR abs/2005.02612 (2020) - [i16]Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama:
Piecewise Linear Regression via a Difference of Convex Functions. CoRR abs/2007.02422 (2020) - [i15]Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen:
Real-time Localized Photorealistic Video Style Transfer. CoRR abs/2010.10056 (2020)
2010 – 2019
- 2019
- [j9]Trevor Campbell, Brian Kulis, Jonathan P. How:
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 41(6): 1338-1352 (2019) - [c34]Fatih Çakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff:
Deep Metric Learning to Rank. CVPR 2019: 1861-1870 - [c33]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. IJCAI 2019: 6013-6019 - [i14]Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis:
Learning Bregman Divergences. CoRR abs/1905.11545 (2019) - [i13]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Peter Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. CoRR abs/1908.07116 (2019) - 2018
- [c32]Ben Usman, Kate Saenko, Brian Kulis:
Stable Distribution Alignment Using the Dual of the Adversarial Distance. ICLR (Workshop) 2018 - [c31]Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis:
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. ISMIR 2018: 182-189 - [c30]Andrew Cutler, Brian Kulis:
Inferring Human Traits from Facebook Statuses. SocInfo (1) 2018: 167-195 - [i12]Andrew Cutler, Brian Kulis:
Inferring Human Traits From Facebook Statuses. CoRR abs/1805.08718 (2018) - [i11]Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis:
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. CoRR abs/1806.09905 (2018) - 2017
- [c29]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. AISTATS 2017: 421-429 - [i10]Ben Usman, Kate Saenko, Brian Kulis:
Stable Distribution Alignment Using the Dual of the Adversarial Distance. CoRR abs/1707.04046 (2017) - [i9]Xide Xia, Brian Kulis:
W-Net: A Deep Model for Fully Unsupervised Image Segmentation. CoRR abs/1711.08506 (2017) - 2016
- [c28]Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy:
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics. ICML 2016: 2673-2681 - [i8]Robert Finn, Brian Kulis:
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models. CoRR abs/1601.02257 (2016) - [i7]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. CoRR abs/1604.02027 (2016) - 2015
- [c27]Robert Finn, Brian Kulis:
A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models. AISTATS 2015 - [c26]Anirban Roychowdhury, Brian Kulis:
Gamma Processes, Stick-Breaking, and Variational Inference. AISTATS 2015 - [c25]Xiangyang Zhou, Jiaxin Zhang, Brian Kulis:
Power-Law Graph Cuts. AISTATS 2015 - [c24]Ke Jiang, Qichao Que, Brian Kulis:
Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval. CVPR 2015: 4933-4941 - 2014
- [j8]Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, Kate Saenko:
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation. Int. J. Comput. Vis. 109(1-2): 28-41 (2014) - [i6]Anirban Roychowdhury, Brian Kulis:
Gamma Processes, Stick-Breaking, and Variational Inference. CoRR abs/1410.1068 (2014) - [i5]Xiangyang Zhou, Jiaxin Zhang, Brian Kulis:
Power-Law Graph Cuts. CoRR abs/1411.1971 (2014) - [i4]Ke Jiang, Qichao Que, Brian Kulis:
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval. CoRR abs/1411.4199 (2014) - 2013
- [j7]Brian Kulis:
Metric Learning: A Survey. Found. Trends Mach. Learn. 5(4): 287-364 (2013) - [c23]Tamara Broderick, Brian Kulis, Michael I. Jordan:
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) 2013: 226-234 - [c22]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS 2013: 449-457 - [c21]Anirban Roychowdhury, Ke Jiang, Brian Kulis:
Small-Variance Asymptotics for Hidden Markov Models. NIPS 2013: 2103-2111 - [i3]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. CoRR abs/1305.6659 (2013) - 2012
- [j6]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning Using a Linear Transformation. J. Mach. Learn. Res. 13: 519-547 (2012) - [j5]Brian Kulis, Kristen Grauman:
Kernelized Locality-Sensitive Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6): 1092-1104 (2012) - [c20]Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko:
Discovering Latent Domains for Multisource Domain Adaptation. ECCV (2) 2012: 702-715 - [c19]Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML 2012 - [c18]Ke Jiang, Brian Kulis, Michael I. Jordan:
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS 2012: 3167-3175 - 2011
- [c17]Matthew E. Taylor, Brian Kulis, Fei Sha:
Metric learning for reinforcement learning agents. AAMAS 2011: 777-784 - [c16]Brian Kulis, Kate Saenko, Trevor Darrell:
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. CVPR 2011: 1785-1792 - [i2]Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. CoRR abs/1111.0352 (2011) - 2010
- [c15]Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell:
Adapting Visual Category Models to New Domains. ECCV (4) 2010: 213-226 - [c14]Brian Kulis, Peter L. Bartlett:
Implicit Online Learning. ICML 2010: 575-582 - [c13]Prateek Jain, Brian Kulis, Inderjit S. Dhillon:
Inductive Regularized Learning of Kernel Functions. NIPS 2010: 946-954
2000 – 2009
- 2009
- [j4]Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon:
Low-Rank Kernel Learning with Bregman Matrix Divergences. J. Mach. Learn. Res. 10: 341-376 (2009) - [j3]Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney:
Semi-supervised graph clustering: a kernel approach. Mach. Learn. 74(1): 1-22 (2009) - [j2]Brian Kulis, Prateek Jain, Kristen Grauman:
Fast Similarity Search for Learned Metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2143-2157 (2009) - [c12]Brian Kulis, Kristen Grauman:
Kernelized locality-sensitive hashing for scalable image search. ICCV 2009: 2130-2137 - [c11]Brian Kulis, Trevor Darrell:
Learning to Hash with Binary Reconstructive Embeddings. NIPS 2009: 1042-1050 - [c10]Brian Kulis, Suvrit Sra, Inderjit S. Dhillon:
Convex Perturbations for Scalable Semidefinite Programming. AISTATS 2009: 296-303 - [i1]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning using a Linear Transformation. CoRR abs/0910.5932 (2009) - 2008
- [c9]Prateek Jain, Brian Kulis, Kristen Grauman:
Fast image search for learned metrics. CVPR 2008 - [c8]Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman:
Online Metric Learning and Fast Similarity Search. NIPS 2008: 761-768 - 2007
- [j1]Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis:
Weighted Graph Cuts without Eigenvectors A Multilevel Approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11): 1944-1957 (2007) - [c7]Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon:
Information-theoretic metric learning. ICML 2007: 209-216 - [c6]Brian Kulis, Arun C. Surendran, John C. Platt:
Fast Low-Rank Semidefinite Programming for Embedding and Clustering. AISTATS 2007: 235-242 - 2006
- [c5]Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon:
Learning low-rank kernel matrices. ICML 2006: 505-512 - 2005
- [c4]Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney:
Semi-supervised graph clustering: a kernel approach. ICML 2005: 457-464 - [c3]Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis:
A fast kernel-based multilevel algorithm for graph clustering. KDD 2005: 629-634 - 2004
- [c2]Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis:
Kernel k-means: spectral clustering and normalized cuts. KDD 2004: 551-556 - 2003
- [c1]John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman:
Natural communities in large linked networks. KDD 2003: 541-546
Coauthor Index
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last updated on 2024-11-15 20:41 CET by the dblp team
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