-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathpubs.html
65 lines (65 loc) · 5.41 KB
/
pubs.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
"http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en">
<head>
<meta name="generator" content="jemdoc, see http://jemdoc.jaboc.net/" />
<meta http-equiv="Content-Type" content="text/html;charset=utf-8" />
<link rel="stylesheet" href="jemdoc.css" type="text/css" />
<title></title>
</head>
<body>
<table summary="Table for page layout." id="tlayout">
<tr valign="top">
<td id="layout-menu">
<div class="menu-category">Home</div>
<div class="menu-item"><a href="index.html" class="current">About me</a></div>
<div class="menu-item"><a href="pubs.html">Publications</a></div>
</td>
<td id="layout-content">
<h2>Detailed Publications</h2>
<h3>Continual Learning</h3>
<ul>
<li><p>Dipam Goswami, Albin Soutif–Cormerais, Yuyang Liu, Sandesh Kamath, Bartłomiej Twardowski, Joost van de Weijer, Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition, (<b>CVPR’24</b>). <a href="https://openaccess.thecvf.com/content/CVPR2024/html/Goswami_Resurrecting_Old_Classes_with_New_Data_for_Exemplar-Free_Continual_Learning_CVPR_2024_paper.html">html</a> <a href="https://arxiv.org/pdf/2405.19074">pdf</a></p>
</li>
<li><p>Sandesh Kamath, Albin Soutif-Cormerais, Joost van de Weijer, Bogdan Raducanu, The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks, CVPR 2024 Workshop on ‘‘Continual Learning in Computer Vision’’, (<b>CVPR-W’24</b>). <a href="https://sites.google.com/view/clvision2024/call-for-papers/accepted-papers">html</a> <a href="https://arxiv.org/pdf/2405.19074">pdf</a></p>
</li>
</ul>
<h3>Generative AI</h3>
<ul>
<li><p>Xide Xu, Muhammad Atif Butt, Sandesh Kamath, Bogdan Raducanu, Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack - NeurIPS 2024 Workshop on ‘‘Safe Generative AI’’.</p>
</li>
</ul>
<h3>Interpretable ML</h3>
<ul>
<li><p>Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N Balasubramanian, Rethinking Robustness of Model Attributions, The 38th Annual AAAI Conference on Artificial Intelligence, (<b>AAAI’24</b>). <a href="https://ojs.aaai.org/index.php/AAAI/article/view/28047">html</a> <a href="https://arxiv.org/pdf/2312.10534">pdf</a> <a href="https://github.com/ksandeshk/LENS">code</a></p>
</li>
<li><p>Sandesh Kamath, Amlan Jyoti, Karthik Balaji Ganesh, Manoj Gayala, Nandita Lakshmi Tunuguntla, Vineeth N Balasubramanian, On the Robustness of Explanations of Deep Neural Network Models: A Survey <a href="https://arxiv.org/pdf/2211.04780">pdf</a></p>
</li>
</ul>
<h3>Adversarial Robustness</h3>
<ul>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Vineeth N Balasubramanian, Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks, Conference on Neural Information Processing Systems (<b>NeurIPS’21</b>) pg 27462-27474. <a href="https://proceedings.neurips.cc/paper_files/paper/2021/hash/e6ff107459d435e38b54ad4c06202c33-Abstract.html">html</a> <a href="https://arxiv.org/pdf/2002.11318">pdf</a> <a href="https://github.com/ksandeshk/spatial-vs-robustness">code</a></p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Vineeth N Balasubramanian, Universalization of any adversarial attack using very few test examples - CODS-COMAD 2022 <b>(Best Paper Award, Research Track)</b>, pages 72-80. <a href="https://dl.acm.org/doi/abs/10.1145/3493700.3493718">html</a> <a href="https://arxiv.org/abs/2005.08632">pdf</a> <a href="https://github.com/ksandeshk/svd-uap">code</a></p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, On Adversarial Robustness of Small vs. Large Batch Training - ICML 2019 Workshop on ‘‘Understanding and Improving Generalization in Deep Learning’’. <a href="https://sites.google.com/view/icml2019-generalization/accepted-papers">html</a> <a href="https://drive.google.com/file/d/1JPf8iBlKmruDnSWWONpZCGu5xmOBtVyY/view?usp=sharing">pdf</a></p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Better Generalization with Adaptive Adversarial Training - ICML 2019 Workshop on ‘‘Understanding and Improving Generalization in Deep Learning’’. <a href="https://sites.google.com/view/icml2019-generalization/accepted-papers">html</a> <a href="https://drive.google.com/file/d/1TtuScpA9luvOrAoQTOYjSVgyuPkt7XyV/view?usp=sharing">pdf</a></p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, Understanding Adversarial Robustness of Symmetric Networks - ICML 2018 Workshop on ‘‘Towards learning with limited labels: Equivariance, Invariance, and Beyond’’. <a href="https://sites.google.com/site/icml18limitedlabels/accepted-papers">html</a></p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, On Universalized Adversarial and Invariant Perturbation, arxiv.2006.04449, 2020. <a href="https://arxiv.org/abs/2006.04449">pdf</a>.</p>
</li>
<li><p>Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, How do SGD hyperparameters in natural training affect adversarial robustness? arxiv.2006.11604, 2020. <a href="https://arxiv.org/abs/2006.11604">pdf</a>.</p>
</li>
</ul>
<div id="footer">
<div id="footer-text">
Page generated 2024-11-04 19:55:09 CET, by <a href="http://jemdoc.jaboc.net/">jemdoc</a>.
</div>
</div>
</td>
</tr>
</table>
</body>
</html>