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Daniel Kang
Daniel Kang
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Title
Cited by
Cited by
Year
NoScope: Optimizing Neural Network Queries over Video at Scale
D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia
Proceedings of the VLDB Endowment 10 (11), 1586-1597, 2017
477*2017
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ...
NIPS ML Systems Workshop, 2017
3642017
MLPerf Training Benchmark
P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ...
arXiv preprint arXiv:1910.01500, 2019
2982019
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-based Video Analytics
D Kang, P Bailis, M Zaharia
PVLDB 13 (4), 2019
196*2019
Testing Robustness Against Unforeseen Adversaries
D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt
arXiv preprint arXiv:1908.08016, 2019
172*2019
Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark
C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ...
ACM SIGOPS Operating Systems Review 53 (1), 14-25, 2019
1272019
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
S Chinchali, A Sharma, J Harrison, A Elhafsi, D Kang, E Pergament, ...
arXiv preprint arXiv:1902.05703, 2019
1152019
Model Assertions for Monitoring and Improving ML Models
D Kang, D Raghavan, P Bailis, M Zaharia
MLSys, 2020
100*2020
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
D Kang, X Li, I Stoica, C Guestrin, M Zaharia, T Hashimoto
arXiv preprint arXiv:2302.05733, 2023
892023
Improved Natural Language Generation via Loss Truncation
D Kang, T Hashimoto
ACL, 2020
842020
LIT: Learned Intermediate Representation Training for Model Compression
A Koratana, D Kang, P Bailis, M Zaharia
International Conference on Machine Learning, 3509-3518, 2019
74*2019
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding
H Zeng, T Hashimoto, DD Kang, DK Gifford
Bioinformatics 32 (4), 490-496, 2016
492016
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
D Kang, A Mathur, T Veeramacheneni, P Bailis, M Zaharia
arXiv preprint arXiv:2007.13005, 2020
452020
Approximate Selection with Guarantees using Proxies
D Kang, E Gan, P Bailis, T Hashimoto, M Zaharia
PVLDB, 2020
322020
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data
D Kang, J Guibas, PD Bailis, T Hashimoto, M Zaharia
Proceedings of the 2022 International Conference on Management of Data, 1934 …, 2022
30*2022
Q-diffusion: Quantizing diffusion models
X Li, Y Liu, L Lian, H Yang, Z Dong, D Kang, S Zhang, K Keutzer
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
282023
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia
MLSys, 2020
252020
Identifying and mitigating the security risks of generative ai
C Barrett, B Boyd, E Bursztein, N Carlini, B Chen, J Choi, AR Chowdhury, ...
Foundations and Trends® in Privacy and Security 6 (1), 1-52, 2023
202023
A synergistic DNA logic predicts genome-wide chromatin accessibility
T Hashimoto, RI Sherwood, DD Kang, N Rajagopal, AA Barkal, H Zeng, ...
Genome research 26 (10), 1430-1440, 2016
202016
Accelerating Approximate Aggregation Queries with Expensive Predicates
D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia
arXiv preprint arXiv:2108.06313, 2021
192021
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Articles 1–20