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Workshop
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Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
Amnon Geifman · Daniel Barzilai · Ronen Basri · Meirav Galun
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Workshop
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Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Seongsik Park · Jeonghee Jo · Jongkil Park · Yeonjoo Jeong · Jaewook Kim · Suyoun Lee · Joon Young Kwak · Inho Kim · Jong-keuk Park · Kyeong Lee · Hwang Weon · Hyun Jae Jang
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Workshop
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HiGen: Hierarchical Graph Generative Networks
Mahdi Karami
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Workshop
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A Representer Theorem for Vector-Valued Neural Networks: Insights on Weight Decay Training and Widths of Deep Neural Networks
Joseph Shenouda · Rahul Parhi · Kangwook Lee · Robert Nowak
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Workshop
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A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Sofiane ENNADIR · Yassine Abbahaddou · Michalis Vazirgiannis · Henrik Boström
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Workshop
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A Simple and Yet Fairly Effective Defense for Graph Neural Networks
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Workshop
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Fri 14:10
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Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks
David Zhang · Miltiadis (Miltos) Kofinas · Yan Zhang · Yunlu Chen · Gertjan Burghouts · Cees Snoek
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Poster
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Tue 17:00
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Deep linear networks can benignly overfit when shallow ones do
Niladri S. Chatterji · Phil Long
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Poster
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Thu 16:30
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Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
Rishabh Tiwari · Pradeep Shenoy
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Poster
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Thu 16:30
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Quantitative Universal Approximation Bounds for Deep Belief Networks
Julian Sieber · Johann Gehringer
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Workshop
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Deep Neuro-Symbolic Weight Learning in Neural Probabilistic Soft Logic
Connor Pryor · Charles Dickens · Lise Getoor
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Oral
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Thu 18:56
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Direct Parameterization of Lipschitz-Bounded Deep Networks
Ruigang Wang · Ian Manchester
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