Sparse Learning
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Benchmarks
Most implemented
Variational Dropout Sparsifies Deep Neural Networks
Rigging the Lottery: Making All Tickets Winners
The State of Sparsity in Deep Neural Networks
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
Papers
Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data
This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and heterogeneous data. Conventional Gaussian models often lack robustness tow…
Sparse LearningHERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards
Search-agent rewards mix answer quality, citation grounding, tool cost, and anti-hacking terms; a high score therefore need not imply that cited evidence was retrieved, and added penalties can cancel. We introduce HERALD…
Sparse LearningAdversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature …
Sparse LearningPhysics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of payload variation, unmodeled coupling, friction, aerodynamic effects, …
Sparse LearningTwo-Valued Symmetric Circulant Matrices: Applications in Deep Learning
Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses serious challenges due to their high storage requirements, computational c…
Medical DiagnosisSparse LearningByzantine-Robust Distributed Sparse Learning Revisited
We revisit Byzantine robust distributed estimation for high-dimensional sparse linear models. By combining local $\ell_1$-regularized robust estimation with robust aggregation at the server, the framework applies to pseu…
Sparse Learning