Meetings
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Modular Duality in Deep Learning
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Approaching Deep Learning through the Spectral Dynamics of Weights
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The importance of discretisation drift in deep learning
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Information-theoretic generalization bounds for black-box learning algorithms
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Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
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Predicting grokking long before it happens
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The geometry of neural nets' parameter spaces under reparametrization
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Can Neural Network Memorization Be Localized?
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DINO v1 and v2: Self-Supervised Vision Transformers
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SGD with Large Step Sizes Learns Sparse Features
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Bottleneck structure in large depth networks
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Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent
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Understanding edge of stability
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Lottery ticket hypothesis and its current state
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Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
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A Loss Curvature Perspective on Training Instability in Deep Learning
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When Are Solutions Connected in Deep Networks?
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From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
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Towards Understanding Sharpness-Aware Minimization
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When Do Neural Networks Outperform Kernel Methods?
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SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
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Does the Data Induce Capacity Control in Deep Learning?
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Deep Ensembles: A Loss Landscape Perspective
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Taxonomizing local versus global structure in neural network loss landscapes
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The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks
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The Geometry of Neural Network Landscapes: Symmetry-Induced Saddles & Global Minima Manifold
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Exploring Generalization in Deep Learning
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