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Reproduction: An Algebraic View of the Expressivity of Recurrent Language Models
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Tashin Ahmed
745H1N
AI & ML interests
Neural Network Architectures
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updated a Space about 3 hours ago
745H1N/repro-rnn-expressivity published a Space about 3 hours ago
745H1N/repro-rnn-expressivity updated a Space about 3 hours ago
745H1N/repro-special-federatedOrganizations
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Reproduction: Server-Proximal Aggregation for Federated Domain-Incremental Learning
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Reproduction: Provable Accuracy Collapse of Embedding-Based Representations
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Reproduction: Deep Learning of Compositional Targets with Hierarchical Spectral Methods
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Reproduction: Even Faster Kernel Matrix Linear Algebra via Density Estimation
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Reproduction: Pruning at Initialisation through the lens of Graphon Limit
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Reproduction: GradientStabilizer: Fix the Norm, Not the Gradient
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Reproduction: Fairness in Aggregation: Optimal Top-k and Improved Full Ranking
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Reproduction: Is Spurious Correlation Removal Always Learnable
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Reproduction: Decision Tree Learning on Product Spaces
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Reproduction: MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method
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Reproduction: Rethinking Neural Network Learning Rates: A Stackelberg Perspective
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Reproduction: Approximation Error Bounds for Holder Class with Transformers
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Reproduction: Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
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Reproduction: What Does Preference Learning Recover from Pairwise Comparison Data
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Reproduction: High-Probability Convergence Guarantees of Decentralized SGD
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Reproduction: Near-Optimal Private Linear Regression via Iterative Hessian Mixing
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Reproduction: Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization
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Reproduction: CLASP: Online learning algorithms for Convex Losses And Squared Penalties
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Reproduction: CLASP: Online learning algorithms for Convex Losses And Squared Penalties
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Repro - FlexRank: Nested Low-Rank Knowledge Decomposition
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Repro - Rex: Reversible Exponential Runge-Kutta Solvers
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Repro - An Exterior Method for Nonnegative Matrix Factorization
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Repro - Why Are Linear RNNs More Parallelizable?
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