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Author: fatih-ilhan

_pages/about.md

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     <b>Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation</b><br>
     T. Huang, S. Hu, <b>F. Ilhan</b>, S. F. Tekin, and L. Liu.
-    <i>International Conference on Learning Representations</i>, 2025. (<b>ICLR oral</b>) (<a href="https://openreview.net/pdf?id=tTPHgb0EtV">paper</a>) (<a href="https://github.com/git-disl/Booster">code</a>)
+    <i>International Conference on Learning Representations</i>, 2025. (<b>ICLR oral</b>) [<a href="https://openreview.net/pdf?id=tTPHgb0EtV">paper</a>] [<a href="https://github.com/git-disl/Booster">code</a>]
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     <b>Resource-Efficient Transformer Pruning for Finetuning of Large Models</b><br>
     <b>F. Ilhan</b>, G. Su, S. F. Tekin, T. Huang, S. Hu, and L. Liu.
-    <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 2024. (<b>CVPR</b>) (<a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Ilhan_Resource-Efficient_Transformer_Pruning_for_Finetuning_of_Large_Models_CVPR_2024_paper.pdf">paper</a>) (<a href="https://github.com/git-disl/recap">code</a>)
+    <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 2024. (<b>CVPR</b>) [<a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Ilhan_Resource-Efficient_Transformer_Pruning_for_Finetuning_of_Large_Models_CVPR_2024_paper.pdf">paper</a>] [<a href="https://github.com/git-disl/recap">code</a>]
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     <b>Adaptive Deep Neural Network Inference Optimization with EENet</b><br>
     <b>F. Ilhan</b>, KH. Chow, S. Hu, T. Huang, S. F. Tekin, W. Wei, Y. Wu, M. Lee, R. Kompella, H. Latapie, G. Liu, L. Liu.
-    <i>IEEE/CVF Winter Conference on Applications of Computer Vision</i>, 2024. (<b>WACV</b>) (<a href="https://openaccess.thecvf.com/content/WACV2024/papers/Ilhan_Adaptive_Deep_Neural_Network_Inference_Optimization_With_EENet_WACV_2024_paper.pdf">paper</a>) (<a href="https://github.com/git-disl/eenet">code</a>)
+    <i>IEEE/CVF Winter Conference on Applications of Computer Vision</i>, 2024. (<b>WACV</b>) [<a href="https://openaccess.thecvf.com/content/WACV2024/papers/Ilhan_Adaptive_Deep_Neural_Network_Inference_Optimization_With_EENet_WACV_2024_paper.pdf">paper</a>] [<a href="https://github.com/git-disl/eenet">code</a>]
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     <b>Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning</b><br>
     T. Huang, S. Hu, <b>F. Ilhan</b>, S. F. Tekin and L. Liu.
-    <i>Thirty-seventh Conference on Neural Information Processing Systems</i>, 2024. (<b>NeurIPS</b>) (<a href="https://openreview.net/pdf?id=RPChapuXlC">paper</a>) (<a href="https://github.com/git-disl/Lisa">code</a>)
+    <i>Thirty-seventh Conference on Neural Information Processing Systems</i>, 2024. (<b>NeurIPS</b>) [<a href="https://openreview.net/pdf?id=RPChapuXlC">paper</a>] [<a href="https://github.com/git-disl/Lisa">code</a>]
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-    <img src="files/ph.png" alt="" style="max-width: 100%; border: 1px solid #eee;">
+    <img src="files/paper_imgs/lisa.png" alt="" style="max-width: 100%; border: 1px solid #eee;">
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     <b>LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity</b><br>
     S. F. Tekin, <b>F. Ilhan</b>, T. Huang, S. Hu and L. Liu.
-    <i>ACL Conference on Empirical Methods in Natural Language Processing</i>, 2024. (<b>EMNLP findings</b>) (<a href="https://openreview.net/forum?id=mG5jikbsaJ#discussion">paper</a>) (<a href="https://github.com/git-disl/llm-topla">code</a>)
+    <i>ACL Conference on Empirical Methods in Natural Language Processing</i>, 2024. (<b>EMNLP findings</b>) [<a href="https://openreview.net/forum?id=mG5jikbsaJ#discussion">paper</a>] [<a href="https://github.com/git-disl/llm-topla">code</a>]
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-    <img src="files/ph.png" alt="" style="max-width: 100%; border: 1px solid #eee;">
+    <img src="files/paper_imgs/llmtopla.png" alt="" style="max-width: 100%; border: 1px solid #eee;">
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     <b>ScaleFL: Resource-Adaptive Federated Learning with Heterogeneous Clients</b><br>
     <b>F. Ilhan</b>, G. Su and L. Liu.
-    <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 2023. (<b>CVPR</b>) (<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.pdf">paper</a>) (<a href="https://github.com/git-disl/scale-fl">code</a>)
+    <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 2023. (<b>CVPR</b>) [<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.pdf">paper</a>] [<a href="https://github.com/git-disl/scale-fl">code</a>]
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     <b>Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training</b><br>
     T. Huang, S. Hu, <b>F. Ilhan</b>, S. F. Tekin and L. Liu.
-    <i>Thirty-seventh Conference on Neural Information Processing Systems</i>, 2023. (<b>Neurips</b>) (<a href="https://openreview.net/pdf?id=V5cQH7JbGo">paper</a>) (<a href="https://github.com/git-disl/Lockdown">code</a>)
+    <i>Thirty-seventh Conference on Neural Information Processing Systems</i>, 2023. (<b>NeurIPS</b>) [<a href="https://openreview.net/pdf?id=V5cQH7JbGo">paper</a>] [<a href="https://github.com/git-disl/Lockdown">code</a>]
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+    <img src="files/paper_imgs/lockdown" alt="" style="max-width: 100%; border: 1px solid #eee;">
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     <b>Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments</b><br>
     <b>F. Ilhan</b>, O. Karaahmetoglu, Ismail Balaban and S. S. Kozat.
-    <i>IEEE Transactions on Neural Networks and Learning Systems</i>, 2021. (<b>IEEE TNNLS</b>) (<a href="https://ieeexplore.ieee.org/document/9509335">paper</a>) (<a href="https://github.com/fatih-ilhan/markov-rnn">code</a>)
+    <i>IEEE Transactions on Neural Networks and Learning Systems</i>, 2021. (<b>IEEE TNNLS</b>) [<a href="https://ieeexplore.ieee.org/document/9509335">paper</a>] [<a href="https://github.com/fatih-ilhan/markov-rnn">code</a>]
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     <b>Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels</b><br>
     <b>F. Ilhan</b> and S. S. Kozat.
-    <i>IEEE Transactions on Signal Processing</i>, 2020. (<b>IEEE TSP</b>) (<a href="https://ieeexplore.ieee.org/document/9177186">paper</a>) (<a href="https://github.com/fatih-ilhan/sthawkes">code</a>)
+    <i>IEEE Transactions on Signal Processing</i>, 2020. (<b>IEEE TSP</b>) [<a href="https://ieeexplore.ieee.org/document/9177186">paper</a>] [<a href="https://github.com/fatih-ilhan/sthawkes">code</a>]
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