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2016-11-04 05:38:56
2025-05-23 04:52:50
ICLR.cc/2025/Conference
NNlg3eUZ8N
cX02yuzwWI
[{'section': '2 RELATED WORK', 'after_section': '2 RELATED WORK', 'context_after': 'Theoretical Frameworks for GNNs. Despite the empirical success of Graph Neural Networks (GNNs), establishing theories to explain their behaviors is still an evolving field. Recent works have made significant progress in understanding ov...
2024-11-26 14:31:47
ICLR.cc/2025/Conference
cX02yuzwWI
UMYtslJUhV
[{'section': '3.1 NOTATIONS AND DEFINITIONS', 'after_section': None, 'context_after': '(C(l) (cid:88) i (t) = t )ij, ', 'paragraph_idx': 11, 'before_section': None, 'context_before': 'This matrix C (l) neously. ', 'modified_lines': 'Definition 9 (Effective Degree). The effective degree degeff degeff ', 'original_lines...
2024-11-26 14:53:38
ICLR.cc/2025/Conference
UMYtslJUhV
U6AVndkUMf
[{'section': '1 INTRODUCTION', 'after_section': None, 'context_after': '1 ', 'paragraph_idx': 5, 'before_section': '1 INTRODUCTION', 'context_before': 'have applied dropout to GNNs, often observing beneficial effects on generalization (Hamilton et al., 2017). ', 'modified_lines': 'While dropout in standard neural netwo...
2024-11-27 13:01:39
ICLR.cc/2025/Conference
U6AVndkUMf
P6MvxEzOug
[]
2025-02-13 10:56:30
ICLR.cc/2025/Conference
P6MvxEzOug
hkDXGVP2rZ
[]
2025-03-01 07:02:13
ICLR.cc/2025/Conference
hkDXGVP2rZ
9hv4yMVvK1
[{'section': 'Abstract', 'after_section': '1 Introduction', 'context_after': 'The remarkable success of deep neural networks across various domains has been accompanied by the persistent challenge of overfitting, where models perform well on training data but fail to generalize to unseen examples. This issue has spurre...
2025-03-01 14:36:20
ICLR.cc/2025/Conference
9hv4yMVvK1
gXXJcVisjV
[{'section': 'Abstract', 'after_section': None, 'context_after': '2 Related Work ', 'paragraph_idx': 2, 'before_section': None, 'context_before': 'ical understanding of regularization in GCNs and paves the way for more principled approaches to leveraging dropout in graph representation learning. Furthermore, we validat...
2025-03-02 07:51:13
ICLR.cc/2025/Conference
gXXJcVisjV
2iIUWdNUNh
[{'section': '1 Introduction', 'after_section': None, 'context_after': '1 ', 'paragraph_idx': 5, 'before_section': '1 Introduction', 'context_before': 'have applied dropout to GNNs, often observing beneficial effects on generalization (Hamilton et al., 2017). ', 'modified_lines': '∗Hao Zhu is the corresponding author a...
2025-03-14 04:30:46
ICLR.cc/2025/Conference
2iIUWdNUNh
oVG40icCJj
[]
2025-03-30 14:03:15
ICLR.cc/2025/Conference
ItPv3YFVp6
oFznv6Hqvv
[{'section': 'Abstract', 'after_section': None, 'context_after': '1 ', 'paragraph_idx': 2, 'before_section': 'Abstract', 'context_before': 'show that SSL can perform system identification in latent space. We propose DYNCL, a framework to uncover linear, switching linear and non-linear dynamics under a non-linear observ...
2025-02-28 23:10:15
ICLR.cc/2025/Conference
oFznv6Hqvv
3EC25XMVtD
[{'section': '1 (7)', 'after_section': None, 'context_after': '4 ', 'paragraph_idx': 25, 'before_section': None, 'context_before': '(7) Note that the dynamics model ˆf (xt; W, zt) depends on an additional latent variable zt = ', 'modified_lines': '[zt,1, . . . , zt,K]⊤ which contains probabilities to parametrize the dy...
2025-03-01 23:06:00
ICLR.cc/2025/Conference
3EC25XMVtD
zVWFIErqTQ
[]
2025-03-01 23:08:16
ICLR.cc/2025/Conference
zVWFIErqTQ
22A0PSumim
[{'section': '1 INTRODUCTION', 'after_section': '1 INTRODUCTION', 'context_after': 'In this work, we revisit and extend contrastive learning in the context of system identification. We uncover several surprising facts about its out-of-the-box effectiveness in identifying dynamics and ', 'paragraph_idx': 4, 'before_sect...
2025-03-15 10:04:11
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