Pipelined Gradient Coding
2026-07-24T08:51:43Z•603b15406ac3621ed2977da4eae4bf81e3302cebd3763c3cdef28708f3e0829c
3D-Gaussian-splattingLLM-assisted-discoveryMixture-of-ExpertsPoisson-VoronoiShannon-capacityacademicarXivchannel-gain-mapsclustering-validationcoding-theorydistributed-trainingelectromagnetic-couplingfluid-antenna-arraysgradient-codinginformation-theorylattice-codesmachine-learningprivate-information-retrievalquantum-error-correctionself-orthogonal-codesstraggler-mitigationweak-PIRwireless
What happened
This arXiv batch (multiple 2026-07-24 postings) spans distributed ML, coding theory, wireless propagation mapping, antenna design, information theory, and combinatorics. Highlights include: a pipelined gradient-coding (GC) scheme that segments gradient evaluation across steps (FR and CR placements) to reduce training time and accelerate convergence versus conventional GC; new criteria and constructions for self-orthogonal linear and minimal codes yielding optimal or near-optimal quantum error-correcting codes; a formal study of graph-based weak PIR (G-WPIR) that trades privacy for improved PIR
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- 603b15406ac3621ed2977da4eae4bf81e3302cebd3763c3cdef28708f3e0829c
- Enrichment time
- 2026-07-24T08:51:43Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.