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ORCIDKoby Bibas
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2020 – today
- 2024
[j1]Shachar Shayovitz
, Koby Bibas, Meir Feder:
Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks. Entropy 26(2): 129 (2024)- 2023
- [c6]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Semi-supervised Adversarial Learning for Complementary Item Recommendation. WWW 2023: 1804-1812 - [i10]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Semi-supervised Adversarial Learning for Complementary Item Recommendation. CoRR abs/2303.05812 (2023) - 2022
- [c5]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Collaborative Image Understanding. CIKM 2022: 77-87 - [i9]Koby Bibas, Meir Feder:
Beyond Ridge Regression for Distribution-Free Data. CoRR abs/2206.08757 (2022) - [i8]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Collaborative Image Understanding. CoRR abs/2210.11907 (2022) - 2021
- [c4]Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan:
Learning Rotation Invariant Features For Cryogenic Electron Microscopy Image Reconstruction. ISBI 2021: 563-566 - [c3]Koby Bibas, Meir Feder, Tal Hassner:
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. NeurIPS 2021: 1179-1191 - [i7]Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan:
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction. CoRR abs/2101.03549 (2021) - [i6]Koby Bibas, Meir Feder:
The Predictive Normalized Maximum Likelihood for Over-parameterized Linear Regression with Norm Constraint: Regret and Double Descent. CoRR abs/2102.07181 (2021) - [i5]Uriya Pesso, Koby Bibas, Meir Feder:
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness. CoRR abs/2109.01945 (2021) - [i4]Koby Bibas, Meir Feder, Tal Hassner:
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. CoRR abs/2110.09246 (2021)
2010 – 2019
- 2019
- [c2]Dotan Kaufman, Koby Bibas, Eran Borenstein, Michael Chertok, Tal Hassner:
Balancing Specialization, Generalization, and Compression for Detection and Tracking. BMVC 2019: 219 - [c1]Koby Bibas, Yaniv Fogel, Meir Feder:
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression. ISIT 2019: 2304-2308 - [i3]Koby Bibas, Yaniv Fogel, Meir Feder:
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks. CoRR abs/1904.12286 (2019) - [i2]Koby Bibas, Yaniv Fogel, Meir Feder:
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression. CoRR abs/1905.04708 (2019) - [i1]Dotan Kaufman, Koby Bibas, Eran Borenstein, Michael Chertok, Tal Hassner:
Balancing Specialization, Generalization, and Compression for Detection and Tracking. CoRR abs/1909.11348 (2019)
Coauthor Index
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